Research in rural education is alive and well; the chapters in this book demonstrate that researchers are doing rural education work that is both rigorous and responsive to (and inclusive of) the communities in which it takes place. They are addressing a broad range of meaningful research questions and using multiple methods to approach the challenges inherent in rural research. In addition, these chapters call us to action to continuously improve our own research work and dissemination. In this final chapter we suggest some lessons for rural researchers taken from the work presented in the book, including the importance of explaining how rural context affected the conduct of the research, considering multidimensional risk in rural environments, including a variety of stakeholders in research partnerships, trying rigorous designs even with small samples, being realistic about rural recruiting costs, and preparing for sustainability after studies end.
Chapter
Anomaly Detection and Diagnosis for Automatic Radio Network Verification
The concept known as Self-Organizing Networks (SON) has been developed for modern radio networks that deliver mobile broadband capabilities. In such highly complex and dynamic networks, changes to the configuration management (CM) parameters for network elements could have unintended effects on network performance and stability. To minimize unintended effects, the coordination of configuration changes before they are carried out and the verification of their effects in a timely manner are crucial. This paper focuses on the verification problem, proposing a novel framework that uses anomaly detection and diagnosis techniques that operate within a specified spatial scope. The aim is to detect any anomaly, which may indicate actual degradations due to any external or system-internal condition and also to characterize the state of the network and thereby determine whether the CM changes negatively impacted the network state. The results, generated using real cellular network data, suggest that the proposed verification framework automatically classifies the state of the network in the presence of CM changes, indicating the root cause for anomalous conditions.
Detection of Demographics and Identity in Spontaneous Speech and Writing
This chapter focuses on the automatic identification of demographic traits and identity in both speech and writing.
Learning Environments
Controlling Haematophagous Insects: The Quality-of-Life Scenario
This chapter highlights the financial and psychological impact of haematophagous insects in urban environments, using bedbugs as an example. The utility of strategies for vector control in the management of haematophagous insects is also discussed.
Methods of Association and Dissociation for Establishing Selective Brain-Behavior Relations
Methods for identifying and understanding brain structure–function relations have evolved over the past century, from astute observations of selective impairments associated with focal brain damage to dissociations measured by combining quantitative neuropsychologic assessment and brain imaging. Enhanced spatial and temporal resolution in brain imaging modalities has led to refined visualization and quantification of the brain’s substructures, microstructural integrity, and functional connectivity of neural networks. The double dissociation model has been a gold standard used to demonstrate that a particular cognitive, emotional, sensory, or motor process is selectively related to a particular brain region or neural network and not to others. This model has provided a fruitful means for testing hypotheses of functional localization and enabled examination and establishment of component processes contributing to complex cognitive and motor functions, parsing multifactorial behaviors and identifying brain regions, and networks subserving these complex abilities. In this chapter we discuss the evolution of the dissociation model and highlight how the modifications of this model are used presently to establish selective brain–behavior relationships in disorders such as chronic alcoholism with a neuropathologic signature but no localizable, space-occupying lesion.
Authentic Student Work Samples Support Formative Assessment in Middle School
Examination of how Assessment Work Sample Method (AWSM) professional development (PD) impacts teacher practice of mathematics formative assessment.
Soft Agents: Exploring Soft Constraints to Model Robust Adaptive Distributed Cyber-Physical Agent Systems
We are interested in principles for designing and building open distributed systems consisting of multiple cyber-physical agents, specifically, where a coherent global view is unattainable and timely consensus is impossible. Such agents attempt to contribute to a system goal by making local decisions to sense and effect their environment based on local information. In this paper we propose a model, formalized in the Maude rewriting logic system, that allows experimenting with and reasoning about designs of such systems. Features of the model include communication via sharing of partially ordered knowledge, making explicit the physical state as well as the cyber perception of this state, and the use of a notion of soft constraints developed by Martin Wirsing and his team to specify agent behavior. The paper begins with a discussion of desiderata for such models and concludes with a small case study to illustrate the use of the modeling framework.
A Distributed Computing Model for Dataflow, Controlflow, and Workflow in Fractionated Cyber-Physical Systems
With the ongoing trend to parallelize computations for scalability, better performance, and reliability, distributed dataflow models are attracting interest at all design levels, ranging from processorarchitectures to local- and wide-area computing clusters in the cloud. Data-driven computation has also been an important paradigm in sensor networks and embedded systems, which have evolved into a larger research effort on networked cyber-physical systems (NCPS), that can sense and affect their environment. Fractionated cyber-physical systems (FCPS) are an interesting subclass of NCPS where the redundancy and diversity of many unreliable and potentially heterogeneous networked components is exploited to improve scalability, reliability, and verifiability of the overall system. In this paper we present the theory of a new distributed computing model for such systems as a first step toward a model-based design methodology for FCPS. To uniformly capture dataflow, controlflow, and workflow, we use a subclass of Petri nets as an intuitive high-level model, which is translated into a weaker model — namely, a new variant of Petri nets that does not make any atomicity assumptions but instead uses a partial order to ensure eventual consistency. In the full version of this paper, we briefly discuss an application to unmanned aerial vehicle (UAV) swarms, which has been implemented on top of a prototype of our theory for both simulation models and real world deployments.
