Balancing Local Assessment and Statewide Testing: Building a Program that Meets Student Needs

This document discusses the components of assessment for those who are considering adding local assessments to required statewide testing of K-12 students. These components include technical adequacy, local-level opportunity, and how to link to state and local standards. Attributes of a model local assessment program are discussed and examples are given. There are also four key questions evaluators and teachers should ask themselves if they are considering a local-level assessment, including the cost feasibility. Educational levels: Graduate or professional

A usability study of online library systems: A case of Sultanah Bahiyah Library, Universiti Utara Malaysia

The purpose of this study was to investigate usability of online library systems in Universiti Utara Malaysia (UUM). This study evaluated the usability of Sultanah Bahiyah Library’s web based systems by investigating the aspects of simplicity, comfort, user friendliness, control, readability, information adequacy/task match, navigability, recognition, access time, relevancy, consistency and visual presentation. This study examined user’s views about the usability of digital libraries whereas current and perceived importance. A sample of 45 students of Master of Business Administration (MBA) has been chosen. The Sultanah Bahiyah Library’s web based systems is very important especially for students and academic staffs of Universiti Utara Malaysia. The usability of the Library’s web based systems makes students easy to connect and for that the website should be helpful and attractive within good contents. The result found that the parallel nature of the users’ current views about the usability of digital libraries and users’ perceived importance of digital library usability allows direct comparison of all usability properties. The overall results yielded significant difference for the variables of user’s current views and perceived importance

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.Comment: The 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE 2018

Banking and Financial Regulation

This chapter provides a basic overview of banking and financial regulation for the forthcoming Oxford Handbook of Law and Economics (Francesco Paris, ed.). Among other things, the chapter compares traditional and shadow banking and their regulation, differentiating “micro prudential” regulation (which focuses on protecting individual components of the financial system, such as banks) and “macro prudential” regulation (which focuses on protecting against systemic risk). The chapter also examines how regulation can help to correct market failures that undermine financial efficiency. In that context, it discusses, among other things, capital requirements, ring-fencing, and stress testing. Finally, the chapter examines how regulation can help to protect against systemic risk, including by addressing potential triggers of systemic risk (such as maturity transformation—the asset-liability mismatch that results from the short-term funding of long-term projects—and limited liability)

The role of capital, liquidity and credit growth in financial crises in Latin America and East Asia

We construct a dataset of bank capital adequacy and liquidity to test their relationships to crises in Asia and Latin America. Event studies, logit and ROC estimations suggest these variables are valuable leading indicators of crises. They can be used to improve Early Warning System design although there are trade-offs between model simplicity, which implies less monitoring costs and complexity which may improve accuracy. There are significant differences between the regions so pooling assumptions are unsound. AUCs show that capital and/or liquidity can be used in a parsimonious model without substantial loss in crisis predictive accuracy. We find no direct role for credit growth in either region. Our results have implications for Asian and Latin American financial regulators concerned with the impacts of Basel III on their banking systems.This work is funded under ESRC Grant No. PTA – 053 – 27 – 0002, entitled “An Investigation into the Causes of Banking Crises and Early Warning System Design”

On the adequacy of current empirical evaluations of formal models of categorization

Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)