Worcester Polytechnic Institute

About: Worcester Polytechnic Institute is a education organization based out in Worcester, Massachusetts, United States. It is known for research contribution in the topics: Population & Data envelopment analysis. The organization has 6270 authors who have published 12704 publications receiving 332081 citations. The organization is also known as: WPI.

The SDN Shuffle: Creating a Moving-Target Defense using Host-based Software-Defined Networking

Abstract: Moving target systems can help defenders limit the utility of reconnaissance for adversaries, hindering the effectiveness of attacks. While moving target systems are a topic of robust research, we find that prior work in network-based moving target defenses has limitations in either scalability or the ability to protect public servers accessible to unmodified clients. In this work, we present a new moving target defense using software-defined networking (SDN) that can service unmodified clients while avoiding scalability limitations. We then evaluate this approach according to seven moving-target properties and evaluate its performance. We find that the approach achieves its security goals while introducing low overheads.

Leveraging machine-learned detectors of systematic inquiry behavior to estimate and predict transfer of inquiry skill

Abstract: We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine-learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand-coding log files, "text replay tagging". This approach led to detectors that can automatically and accurately identify these inquiry skills under student-level cross-validation. The resulting detectors can be applied at run-time to drive scaffolding intervention. They can also be leveraged to automatically score all practice attempts, rather than hand-classifying them, and build models of latent skill proficiency. As part of this work, we also compared two approaches for doing so, Bayesian Knowledge-Tracing and an averaging approach that assumes static inquiry skill level. These approaches were compared on their efficacy at predicting skill before a student engages in an inquiry activity, predicting performance on a paper-style multiple choice test of inquiry, and predicting performance on a transfer task requiring data collection skills. Overall, we found that both approaches were effective at estimating student skills within the environment. Additionally, the models' skill estimates were significant predictors of the two types of inquiry transfer tests.

Automatic emotion detection in text streams by analyzing Twitter data

Abstract: Techniques to detect the emotions expressed in microblogs and social media posts have a wide range of applications including, detecting psychological disorders such as anxiety or depression in individuals or measuring the public mood of a community A major challenge for automated emotion detection is that emotions are subjective concepts with fuzzy boundaries and with variations in expression and perception To address this issue, a dimensional model of affect is utilized to define emotion classes Further, a soft classification approach is proposed to measure the probability of assigning a message to each emotion class We develop and evaluate a supervised learning system to automatically classify emotion in text stream messages Our approach includes two main tasks: an offline training task and an online classification task The first task creates models to classify emotion in text messages For the second task, we develop a two-stage framework called EmotexStream to classify live streams of text messages for the real-time emotion tracking Moreover, we propose an online method to measure public emotion and detect emotion burst moments in live text streams

Influence of Anode Flow Rate and Cathode Oxygen Pressure on CO Poisoning of Proton Exchange Membrane Fuel Cells

Abstract: The anode flow rate of a proton exchange membrane (PEM) fuel cell involving Pt anode electrocatalyst is found to strongly influence the single cell performance when H 2 containing trace amounts of CO is used as the feed. The performance drops dramatically due to CO poisoning as the anode flow rate increases until a large overpotential is reached when it levels off. This effect of the flow rate on the extent of poisoning is found to he reversible and is explained as depending on the actual concentration of CO in the anode chamber which in turn depends on the feed content. the flow rate, and CO oxidation kinetics on Pt. Further. it is found that oxygen permeating across the PEM from the cathode side also appreciably affects the anode overpotential by providing another route for CO oxidation. A CO inventory model is provided that explains the observed phenomena in a PEM fuel cell operating with H 2 /CO as anode feed and a cathode feed with different oxygen pressures.