Smart School Budgeting: Resources for Districts

In an era of aggressive public education reform, school districts face increasing pressure to produce higher levels of student performance with increasingly limited resources. The economic downturn has forced many districts to tighten their belts, and careful thought must be given to how each and every dollar is spent. Optimally, district leaders should work with stakeholders in their communities to set goals, analyze current spending, provide transparency in their budgeting, and consider cost-saving and reallocation strategies. The Rennie Center has created a toolkit, Smart School Budgeting: Resources for Districts, aiming to assist district leaders in decision-making about school budgeting. Smart School Budgeting is intended to push school leaders to take a more deliberative approach to school budgeting. The resources presented in the toolkit act as a starting point for districts examining their own budgeting processes. The document is designed as a user-friendly summary of existing literature and tools on school finance, budgeting, and resource allocation that directs district leaders and school business officials to practical and useful information to shape resource decisions. Each section includes an overview of a critical topic in school budgeting, summaries of useful documents and resources, relevant case studies (if available), and a resource list with hyperlinked documents for easy access. The toolkit is organized around the following topics: introduction and context for school budget analysis; setting goals; types of budgets; strategies for analyzing spending; tools for budget analysis; and cost-saving strategies.This toolkit was released at a public event on October 3, 2012

Implementasi Pembagian Alokasi Air Bersih kepada Masyarakat di Daerah Rawan Bencana Kota Tangerang Selatan

The main difficulties in post-disaster are the lack of clean water availability, the residents have difficulty getting clean water, both for residents who do not want to leave their settlements, or for those who fled to other places. For example, during a flood, dug wells and pump wells submerge floodwaters in a few days, besides that in the evacuation places there are no clean water or adequate sanitation facilities. Therefore, the provision of clean water is absolutely necessary in disaster-prone areas. Provision can be done with water filtration facilities around the area or it can be a clean water tanker that can be distributed to people in need. The purpose of this study is to describe the implementation of the distribution of clean water allocation to communities in disaster prone areas as well as the supporting and inhibiting factors of such implementation. The research method is qualitative with data collection techniques from interviews, observations and documents studies. Data obtained with an interactive model based on three research focus variables namely, communication and coordination, resource support and disposition. The results showed that communication and coordination as a connecting channel between actors can foster a good disposition. Resource support has a major influence on whether or not the objectives of the allocation of clean water are to be achieved. Whereas the allocation disposition shows the implementor’s ability to respond and represent. Does not rule out the possibility of the three variables that grow factors supporting and inhibiting that can be used as consideration for overcoming problems that arise

A cross-layer approach to enhance QoS for multimedia applications over satellite

The need for on-demand QoS support for communications over satellite is of primary importance for distributed multimedia applications. This is particularly true for the return link which is often a bottleneck due to the large set of end-users accessing a very limited uplink resource. Facing this need, Demand Assignment Multiple Access (DAMA) is a classical technique that allows satellite operators to offer various types of services, while managing the resources of the satellite system efficiently. Tackling the quality degradation and delay accumulation issues that can result from the use of these techniques, this paper proposes an instantiation of the Application Layer Framing (ALF) approach, using a cross-layer interpreter(xQoS-Interpreter). The information provided by this interpreter is used to manage the resource provided to a terminal by the satellite system in order to improve the quality of multimedia presentations from the end users point of view. Several experiments are carried out for different loads on the return link. Their impact on QoS is measured through different application as well as network level metrics

Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability

Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive and Scalable Communication Network

Multi-View Video Packet Scheduling

In multiview applications, multiple cameras acquire the same scene from different viewpoints and generally produce correlated video streams. This results in large amounts of highly redundant data. In order to save resources, it is critical to handle properly this correlation during encoding and transmission of the multiview data. In this work, we propose a correlation-aware packet scheduling algorithm for multi-camera networks, where information from all cameras are transmitted over a bottleneck channel to clients that reconstruct the multiview images. The scheduling algorithm relies on a new rate-distortion model that captures the importance of each view in the scene reconstruction. We propose a problem formulation for the optimization of the packet scheduling policies, which adapt to variations in the scene content. Then, we design a low complexity scheduling algorithm based on a trellis search that selects the subset of candidate packets to be transmitted towards effective multiview reconstruction at clients. Extensive simulation results confirm the gain of our scheduling algorithm when inter-source correlation information is used in the scheduler, compared to scheduling policies with no information about the correlation or non-adaptive scheduling policies. We finally show that increasing the optimization horizon in the packet scheduling algorithm improves the transmission performance, especially in scenarios where the level of correlation rapidly varies with time