Peer assessment as collaborative learning

Peer assessment is an important component of a more participatory culture of learning. The articles collected in this special issue constitute a representative kaleidoscope of current research on peer assessment. In this commentary, we argue that research on peer assessment is currently in a stage of adolescence, grappling with the developmental tasks of identity formation and affiliation. Identity formation may be achieved by efforts towards a shared terminology and joint theory building, whereas affiliation may be reached by a more systematic consideration of research in related fields. To reach identity formation and affiliation, preliminary ideas for a cognitively toned, process-related model of peer assessment and links to related research fields, especially to research on collaborative learning, are presented

P and M class phasor measurement unit algorithms using adaptive cascaded filters

The new standard C37.118.1 lays down strict performance limits for phasor measurement units (PMUs) under steady-state and dynamic conditions. Reference algorithms are also presented for the P (performance) and M (measurement) class PMUs. In this paper, the performance of these algorithms is analysed during some key signal scenarios, particularly those of off-nominal frequency, frequency ramps, and harmonic contamination. While it is found that total vector error (TVE) accuracy is relatively easy to achieve, the reference algorithm is not able to achieve a useful ROCOF (rate of change of frequency) accuracy. Instead, this paper presents alternative algorithms for P and M class PMUs which use adaptive filtering techniques in real time at up to 10 kHz sample rates, allowing consistent accuracy to be maintained across a ±33% frequency range. ROCOF errors can be reduced by factors of >40 for P class and >100 for M class devices

Link Invariants for Flows in Higher Dimensions

Linking numbers in higher dimensions and their generalization including gauge fields are studied in the context of BF theories. The linking numbers associated to $n$-manifolds with smooth flows generated by divergence-free p-vector fields, endowed with an invariant flow measure are computed in different cases. They constitute invariants of smooth dynamical systems (for non-singular flows) and generalizes previous results for the 3-dimensional case. In particular, they generalizes to higher dimensions the Arnold's asymptotic Hopf invariant for the three-dimensional case. This invariant is generalized by a twisting with a non-abelian gauge connection. The computation of the asymptotic Jones-Witten invariants for flows is naturally extended to dimension n=2p+1. Finally we give a possible interpretation and implementation of these issues in the context of string theory.Comment: 21+1 pages, LaTeX, no figure

Scather: programming with multi-party computation and MapReduce

We present a prototype of a distributed computational infrastructure, an associated high level programming language, and an underlying formal framework that allow multiple parties to leverage their own cloud-based computational resources (capable of supporting MapReduce [27] operations) in concert with multi-party computation (MPC) to execute statistical analysis algorithms that have privacy-preserving properties. Our architecture allows a data analyst unfamiliar with MPC to: (1) author an analysis algorithm that is agnostic with regard to data privacy policies, (2) to use an automated process to derive algorithm implementation variants that have different privacy and performance properties, and (3) to compile those implementation variants so that they can be deployed on an infrastructures that allows computations to take place locally within each participant’s MapReduce cluster as well as across all the participants’ clusters using an MPC protocol. We describe implementation details of the architecture, discuss and demonstrate how the formal framework enables the exploration of tradeoffs between the efficiency and privacy properties of an analysis algorithm, and present two example applications that illustrate how such an infrastructure can be utilized in practice.This work was supported in part by NSF Grants: #1430145, #1414119, #1347522, and #1012798