A framework and a tool to generate e-business options

In early stages, many organizations started to use the internet in more or less ad hoc and experimental ways. After this first stage of learning and experimentation there often arises a need for more systematic approaches to identify, order, and assess e-business options. This paper addresses this need and presents a framework as well as a tool supporting this framework, helping management to generate and order e-business options for their organization. The framework consists of two parts. The first part covers the identification of the dimensions of e-business options. Six dimensions are identified: external stakeholders groups, stakeholder statuses, channel strategies, communication modes, products/service groups, and product/service statuses. Users of this framework can apply these dimensions given the specific characteristics of the organization at hand. Subsequently, these dimensions are combined, generating, in many cases, a multitude of potential e-business options. The second part of the framework supports the process of ordering this large set of generated potential e-business options given certain criteria. This can be accomplished by ordering the dimensions as well as the elements along each distinguished dimension. Some of these elements are company-independent, while others are company-dependent. The framework is illustrated by a case study as a running example. We also offer a design of a tool supporting our framework. The framework focuses on e-business options between an organization and its current or new external stakeholders: possible internal e-business applications are excluded in this paper. The framework can be used as a tool for practitioners, such as consultants or managers, to generate e-business options for a company. They can use it -for example- in workshops to support idea-generation with respect to e-business planning in a creative and structured way. The framework also contributes to theory by providing a method that systematically offers new possibilities for using the internet. After the identification and the ordering of e-business options, the generated and ordered options have to be assessed and selected; this paper however, only focuses on the generating and ordering process.

New literacies: everyday practices and social learning, 3rd edition

The new edition of this popular book takes a fresh look at what it means to think of literacies as social practices. The book explores what is distinctively 'new' within a range of currently popular everyday ways of generating, communicating and negotiating meanings. Revised, updated and significantly reconceptualised throughout, the book includes: * Closer analysis of new literacies in terms of active collaboration * A timely discussion of using wikis and other collaborative online writing resources * Updated and expanded accounts of digital remix and blogging practices * An explanation of social learning and collaborative platforms for social learning * A fresh focus on online social networking * A new batch of discussion questions and stimulus activities The importance of social learning for becoming proficient in many new literacy practices, and the significance of new media for expanding the reach and potential of social learning are discussed in the final part of the book. New Literacies 3/e concludes by describing empirical cases of social learning approaches mediated by collaborative learning platforms. This book is essential reading for students and academics within literacy studies, cultural or communication studies and education

A generalized e-learning usage behaviour model by data mining technique

Current study on e-Learning user’s behaviour model obtained the specific models.In many cases, the e-Learning user’s behaviour model for open source e-Learning system such as Moodle, which can predict learning outcome or learning performance is still deficient and cannot generally apply in many institutions due to the fact that the majority of prediction models were developed particularly for certain institutions. This study proposes to produce a general model that can make a prediction of learning outcome inspired by Skinner’s theory, which explains the relationship between learner, achievement, and learner reinforcement.This study proposes similar patterns in e-Learning user’s behaviour models of different institutions by the data-mining technique based on the learning environment theory.Therefore, this research is conducted in three main phases; include data preparation from weblog of different institutions with the same e-Learning system, data extraction by the accurate classifier model finding process and model verification for generating a verification pattern.The research outcome will be a similar pattern that could be used as a direction for creating a more appropriate e-Learning users’ behaviour model and could be used broadly in other higher institutions

full-FORCE: A Target-Based Method for Training Recurrent Networks

Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable "target" dynamics useful for performing the task. Because it exploits the full recurrent connectivity, the method produces networks that perform tasks with fewer neurons and greater noise robustness than traditional least-squares (FORCE) approaches. In addition, we show how introducing additional input signals into the target-generating network, which act as task hints, greatly extends the range of tasks that can be learned and provides control over the complexity and nature of the dynamics of the trained, task-performing network.Comment: 20 pages, 8 figure

Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction. Previous research on learning-based referring expressions has focused primarily on comprehension tasks, while generating referring expressions is still mostly limited to rule-based methods. In this work, we propose a two-stage approach that relies on deep learning for estimating spatial relations to describe an object naturally and unambiguously with a referring expression. We compare our method to the state of the art algorithm in ambiguous environments (e.g., environments that include very similar objects with similar relationships). We show that our method generates referring expressions that people find to be more accurate ($\sim$30% better) and would prefer to use ($\sim$32% more often).Comment: International Conference on Intelligent Robots and Systems (IROS 2019), Demo 1: Finding the described object (https://youtu.be/BE6-F6chW0w), Demo 2: Referring to the pointed object (https://youtu.be/nmmv6JUpy8M), Supplementary Video (https://youtu.be/sFjBa_MHS98

Folk intuitions of Actual Causation: A Two-Pronged Debunking Explanation

How do we determine whether some candidate causal factor is an actual cause of some particular outcome? Many philosophers have wanted a view of actual causation which fits with folk intuitions of actual causation and those who wish to depart from folk intuitions of actual causation are often charged with the task of providing a plausible account of just how and where the folk have gone wrong. In this paper, I provide a range of empirical evidence aimed at showing just how and where the folk go wrong in determining whether an actual causal relation obtains. The evidence suggests that folk intuitions of actual causation are generated by two epistemically defective processes. I situate the empirical evidence within a background discussion of debunking, arguing for a two-pronged debunking explanation of folk intuitions of actual causation. I conclude that those who wish to depart from folk intuitions of actual causation should not be compelled to square their account of actual causation with the verdicts of the folk. In the dispute over actual causation, folk intuitions deserve to be rejected

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

Machine Learning with Abstention for Automated Liver Disease Diagnosis

This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.Comment: Preprint version before submission for publication. complete version published in proc. 15th International Conference on Frontiers of Information Technology (FIT 2017), December 18-20, 2017, Islamabad, Pakistan. http://ieeexplore.ieee.org/document/8261064

Learning and generation of long-range correlated sequences

We study the capability to learn and to generate long-range, power-law correlated sequences by a fully connected asymmetric network. The focus is set on the ability of neural networks to extract statistical features from a sequence. We demonstrate that the average power-law behavior is learnable, namely, the sequence generated by the trained network obeys the same statistical behavior. The interplay between a correlated weight matrix and the sequence generated by such a network is explored. A weight matrix with a power-law correlation function along the vertical direction, gives rise to a sequence with a similar statistical behavior.Comment: 5 pages, 3 figures, accepted for publication in Physical Review