Individual emergence in contextual analysis

Located within the tradition of Hermeneutic Dialectics (HD) this paper offers an approach which can further an analysis of a fit between information and organizational systems. Drawn upon Information Systems Development projects a relationship between theory and practice is aided through a multi-disciplinary approach to sense making activity. Using a contemporary version of contextual analysis to understand a way in which individuals construct adapt and create meaning from their environment offers a route to improve a systems analysis process. This type of enquiry into contextual dependencies of knowledge creation can help direct a development of systems that have the intention to serve specific organizational actors and their needs. Combining methods outside of a traditional polar divide, sense making research undertaken within a systems thinking arena can enrich understanding by complementing qualitative and / or quantitative analysis with reflective depth. Drawing together interdisciplinary strands through a critical systems thinking approach offers new levels of professionalism for computer- and management-, practitioners or researchers in the 21st Century

The Use of Contextual Analysis in Teaching Vocabulary

The purpose of this research is to find out the effectiveness of the use of contextual analysis in teaching vocabulary to the tenth grade students of MAN 2 Pontianak in academic year 2013/2014. This research was conducted as a pre experimental research with the one group pretest-posttest. The sample of this research were class X MIA 1 as the experimental class. The data were collected through pretest and posttest by using multiple choice consisting of twenty items and were analyzed by using Effect Size (ES) formula. The finding shows that the effect of treatment is 1.3 (>1.00) or categorized as strong effect. It indicates that the use of contextual analysis is effective in teaching vocabulary

Location data and privacy: a framework for analysis

Innovative services have exploited data about users’ physical location, sometimes but not always explicitly with their consent. As new applications that reveal users’ location data appear on the Web it essential to focus on the privacy implications, in particular with respect to inferences about context. This paper focuses on the understanding of location and contextual privacy by developing a framework for analysis, which is applied to existing systems that exploit location data. The analysis highlights the primal role of location in linking and inferring contextual data, but also how these inferences can extend to non-contextual data

[Review of] Ken Goodwin. Understanding African Poetry: A Study of Ten Poets

Understanding African Poetry is a valuable asset to anyone interested in African anglophone poetry. Goodwin offers textual analysis, evaluation, and supplementary contextual information on each of the ten poets he chose to discuss. Much of the analysis shows a keen insight and the contextual commentary is quite informative. However, Goodwin\u27s evaluation reflects his bias towards British and white American concepts of what constitutes good poetry

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft

Effect of Contextual Learning Ability Against Students Understanding Math Concepts SMP

This study aims to determine whether or not there is the influence of contextual learning of math concepts students' comprehension ability. The subject of this study is the seventh grade students of SMP Negeri 10 Palembang. The research method used in this study is an experiment. The variables of this study was the ability of understanding the concept of students. Methods of data collection using a written test, the data obtained by using t test analysis. The results of this study found that there is the influence of contextual learning on the ability of junior high school students’ understanding of mathematical concepts. Key Words: Contextual Learning, understanding the concep

Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction

We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework. The model learns latent contextual representations by maximizing the predictability of visual events based on local and global contextual information through both top-down and bottom-up processes. In contrast to standard predictive coding models, the prediction error in this model is used to update the contextual representation but does not alter the feedforward input for the next layer, and is thus more consistent with neurophysiological observations. We establish the computational feasibility of this model by demonstrating its ability in several aspects. We show that our model can outperform state-of-art performances of gated Boltzmann machines (GBM) in estimation of contextual information. Our model can also interpolate missing events or predict future events in image sequences while simultaneously estimating contextual information. We show it achieves state-of-art performances in terms of prediction accuracy in a variety of tasks and possesses the ability to interpolate missing frames, a function that is lacking in GBM