Mediating Power: Distance Learning Interfaces, Classroom Epistemology, and the Gaze

Distance learning's interfaces—from corresponding through the postal service to the televised talking head—have traditionally been designed from the top down, supporting banking models of learning or, in writing instruction, current-traditional rhetoric pedagogies. Due to temporal and spatial constraints, these interface designs often support (or encourage) one-way communication from the instructor to the student. Students mostly interact with the instructor by asking questions or submitting work, and they tend to have little correspondence with other peers. These methods clearly privilege the instructor's knowledge and evaluation. Furthermore, these interface designs empower the instructor to gaze upon the students and assess them—often not as a corporeal body but as a corpus of texts. Thus, each interface adopted for distance learning sets up a power dynamic in which the capability to share the roles of creating knowledge is juxtaposed with the instructor's capability to normalize the students and reify their own authority through their gaze. In this article we examine the traditional classroom interface through the correspondence course interface, the simulated classroom interface, and the synchronous video interface to raise questions about the infrastructures of distance learning and their implications for student learning.     

The effect of floor slope on sub-maximal lifting capacity and technique

Inclined surfaces, where both the lifter and load are on the slope, may be encountered in a jobsite situation. The purpose of this study was to determine if facing up or down a sloped surface (10 degrees and 20 degrees ) would affect maximal acceptable weights of lift (MAWL) using a 10 min psychophysical approach with symmetric freestyle technique at 4 lifts/min. Seventeen healthy men and 18 women determined floor to knuckle height MAWL while facing uphill, downhill, and on a level surface. Motion capture was also performed to examine sagittal plane joint angles and foot placement relative to a milk crate. Slope did not alter MAWL (p>0.05) with the men lifting more than the women in every condition (p<0.001) (25 kg vs. 15 kg, respectively). Foot placement relative to the box was altered by slope such that both horizontal position behind and vertical position below the box increased as slope changed from the downhill to uphill conditions (both p<0.001). Forward torso lean as well as hip, knee, and ankle (plantar) flexion generally decreased as slope changed from the downhill to uphill conditions (all p<0.001). Torso and knee motion appeared to be protected compared to the other joints, changing the least. Though trends were the same in both sexes, interactions did exist in vertical foot position and hip angle (both p0.001). In conclusion, the body is highly adaptive to floor slope, maintaining MAWL at least in the short term. However, while slight technique differences exist between men and women, care should be taken by all when facing uphill due to the tendency to stand further from the load horizontally and when facing downhill due to increased torso lean.     

不同无线技术能否共存?

新建工厂的无线技术应用已逐步获得认可,然而,还有一些过程工业对采用这项新技术或其它新的解决方案存有疑虑,主要担心多种无线技术互相之间可能存在射频干扰而影响主要通讯的可靠性,例如使用IEEE 802.11b/g和IEEE 802.15.4协议的无线电。因为两种无线电通讯采用的也是用于工业、科技和医疗的2.4GHz波段,业内人士曾     

NETMET: A program for generating and interpreting metaphors

Metaphors have computable semantics. A program called NETMET both generates metaphors and produces partial literal interpretations of metaphors. NETMET is based on Kittay's semantic field theory of metaphor and Black's interaction theory of metaphor. Input to NETMET consists of a list of literal propositions. NETMET creates metaphors by finding topic and source semantic fields, producing an analogical map from source to topic, then generating utterances in which terms in the source are identified with or predicated of terms in the topic. Given a metaphor, NETMET utilizes if-then rules to generate the implication complex of that metaphor. The literal leaves of the implication complex comprise a partial literal interpretation.Eric Steinhart has an MA in philosophy (Boston College) and completed his PhD dissertation (Formal Semantics for Metaphors) at SUNY at Stony Brook. He has written on technology, analogy, metaphor and idealism. His publications include Beyond the Proportional Analogy: A Structural Model of AnalogyPragmatics and Cognition, 2, 1 (1994). He is interested in the application of computational methods to philosophical topics and is working on a computational model of idealistic metaphysics.     

Experience report on the application of genetic algorithms to reduce costs of the software validation process in the automotive sector during an engine control unit project

Software Quality Journal - The number of electronic control units (ECU) installed in vehicles is increasingly high. Manufacturers must improve the software quality and reduce cost by proposing...     

Polynomial regression under arbitrary product distributions

In recent work, Kalai, Klivans, Mansour, and Servedio (2005) studied a variant of the “Low-Degree (Fourier) Algorithm” for learning under the uniform probability distribution on {0,1} ⁿ . They showed that the L ₁ polynomial regression algorithm yields agnostic (tolerant to arbitrary noise) learning algorithms with respect to the class of threshold functions—under certain restricted instance distributions, including uniform on {0,1} ⁿ and Gaussian on ? ⁿ . In this work we show how all learning results based on the Low-Degree Algorithm can be generalized to give almost identical agnostic guarantees under arbitrary product distributions on instance spaces X ₁×???×X _n . We also extend these results to learning under mixtures of product distributions. The main technical innovation is the use of (Hoeffding) orthogonal decomposition and the extension of the “noise sensitivity method” to arbitrary product spaces. In particular, we give a very simple proof that threshold functions over arbitrary product spaces have δ-noise sensitivity $O(\sqrt{\delta})$ , resolving an open problem suggested by Peres (2004).     

Action selection and task sequence learning for hybrid dynamical cognitive agents

As a foundation for action selection and task-sequencing intelligence, the reactive and deliberative subsystems of a hybrid agent can be unified by a single, shared representation of intention. In this paper, we summarize a framework for hybrid dynamical cognitive agents (HDCAs) that incorporates a representation of dynamical intention into both reactive and deliberative structures of a hybrid dynamical system model, and we present methods for learning in these intention-guided agents. The HDCA framework is based on ideas from spreading activation models and belief–desire–intention (BDI) models. Intentions and other cognitive elements are represented as interconnected, continuously varying quantities, employed by both reactive and deliberative processes. HDCA learning methods—such as Hebbian strengthening of links between co-active elements, and belief–intention learning of task-specific relationships—modify interconnections among cognitive elements, extending the benefits of reactive intelligence by enhancing high-level task sequencing without additional reliance on or modification of deliberation. We also present demonstrations of simulated robots that learned geographic and domain-specific task relationships in an office environment.     

Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS,Python, R,MATLAB, and C++

With the arrival of GPS, satellite remote sensing, and personal computers, the last two decades have witnessed rapid advances in the field of spatially-explicit marine ecological modeling. But with this innovation has come complexity. To keep up, ecologists must master multiple specialized software packages, such as ArcGIS for display and manipulation of geospatial data, R for statistical analysis, and MATLAB for matrix processing. This requires a costly investment of time and energy learning computer programming, a high hurdle for many ecologists. To provide easier access to advanced analytic methods, we developed Marine Geospatial Ecology Tools (MGET), an extensible collection of powerful, easy-to-use, open-source geoprocessing tools that ecologists can invoke from ArcGIS without resorting to computer programming. Internally, MGET integrates Python, R, MATLAB, and C++, bringing the power of these specialized platforms to tool developers without requiring developers to orchestrate the interoperability between them.In this paper, we describe MGET’s software architecture and the tools in the collection. Next, we present an example application: a habitat model for Atlantic spotted dolphin (Stenella frontalis) that predicts dolphin presence using a statistical model fitted with oceanographic predictor variables. We conclude by discussing the lessons we learned engineering a highly integrated tool framework.