17,847 research outputs found
Ausubel's meaningful learning re-visited
This review provides a critique of David Ausubel’s theory of meaningful learning and the use of advance organizers in teaching. It takes into account the developments in cognition and neuroscience which have taken place in the 50 or so years since he advanced his ideas, developments which challenge our understanding of cognitive structure and the recall of prior learning. These include (i) how effective questioning to ascertain previous knowledge necessitates in-depth Socratic dialogue; (ii) how many findings in cognition and neuroscience indicate that memory may be non-representational, thereby affecting our interpretation of student recollections; (iii) the now recognised dynamism of memory; (iv) usefully regarding concepts as abilities or simulators and skills; (v) acknowledging conscious and unconscious memory and imagery; (vi) how conceptual change involves conceptual coexistence and revision; (vii) noting linguistic and neural pathways as a result of experience and neural selection; and (viii) recommending that wider concepts of scaffolding should be adopted, particularly given the increasing focus on collaborative learning in a technological world
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
Subsidiary Entrepreneurial Alertness: Antecedents and Outcomes
This thesis brings together concepts from both international business and entrepreneurship to develop a framework of the facilitators of subsidiary innovation and performance. This study proposes that Subsidiary Entrepreneurial Alertness (SEA) facilitates the recognition of opportunities (the origin of subsidiary initiatives). First introduced by Kirzner (1979) in the context of the individual, entrepreneurial alertness (EA) is the ability to notice an opportunity without actively searching. Similarly, to entrepreneurial alertness at the individual level, this study argues that SEA enables the subsidiary to best select opportunities based on resources available. The research further develops our conceptualisation of SEA by drawing on work by Tang et al. (2012) identifying three distinct activities of EA: scanning and search (identifying opportunities unseen by others due to their awareness gaps), association and connection of information, and evaluation and judgement to interpret or anticipate future viability of opportunities. This study then hypothesises that SEA leads to opportunity recognition at the subsidiary level and further hypothesises innovation and performance as outcomes of opportunity recognition. This research brings these arguments together to develop and test a comprehensive theoretical model.
The theoretical model is tested through a mail survey of the CEOs/MDs of foreign subsidiaries within the Republic of Ireland (an innovative hub for foreign subsidiaries). This method was selected as the best method to reach the targeted respondent, and due to the depth of knowledge the target respondent holds, the survey can answer the desired question more substantially. The results were examined using partial least squares structural equation modelling (PLS-SEM). The study’s findings confirm two critical aspects of subsidiary context, subsidiary brokerage and subsidiary credibility are positively related to SEA. The study establishes a positive link between SEA and both the generation of innovation and the subsidiary’s performance. This thesis makes three significant contributions to the subsidiary literature as it 1) introduces and develops the concept of SEA, 2) identifies the antecedents of SEA, and 3) demonstrates the impact of SEA on subsidiary opportunity recognition. Implications for subsidiaries, headquarters and policy makers are discussed along with the limitations of the study
Self-Ordering Point Clouds
In this paper we address the task of finding representative subsets of points
in a 3D point cloud by means of a point-wise ordering. Only a few works have
tried to address this challenging vision problem, all with the help of hard to
obtain point and cloud labels. Different from these works, we introduce the
task of point-wise ordering in 3D point clouds through self-supervision, which
we call self-ordering. We further contribute the first end-to-end trainable
network that learns a point-wise ordering in a self-supervised fashion. It
utilizes a novel differentiable point scoring-sorting strategy and it
constructs an hierarchical contrastive scheme to obtain self-supervision
signals. We extensively ablate the method and show its scalability and superior
performance even compared to supervised ordering methods on multiple datasets
and tasks including zero-shot ordering of point clouds from unseen categories
An Experimental Study on Sentiment Classification of Moroccan dialect texts in the web
With the rapid growth of the use of social media websites, obtaining the
users' feedback automatically became a crucial task to evaluate their
tendencies and behaviors online. Despite this great availability of
information, and the increasing number of Arabic users only few research has
managed to treat Arabic dialects. The purpose of this paper is to study the
opinion and emotion expressed in real Moroccan texts precisely in the YouTube
comments using some well-known and commonly used methods for sentiment
analysis. In this paper, we present our work of Moroccan dialect comments
classification using Machine Learning (ML) models and based on our collected
and manually annotated YouTube Moroccan dialect dataset. By employing many text
preprocessing and data representation techniques we aim to compare our
classification results utilizing the most commonly used supervised classifiers:
k-nearest neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and
deep learning (DL) classifiers such as Convolutional Neural Network (CNN) and
Long Short-Term Memory (LTSM). Experiments were performed using both raw and
preprocessed data to show the importance of the preprocessing. In fact, the
experimental results prove that DL models have a better performance for
Moroccan Dialect than classical approaches and we achieved an accuracy of 90%.Comment: 13 pages, 5 tables, 2 figure
Creating an Information System with a Social Purpose - The Case of Re-Food
Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementNowadays, with the evolution of technology, the strategies of information management are
constantly improving to create faster and more efficient methods of analyzing and displaying data. At
the same time, the users, considering the amount of information they access every day, search for
simpler, faster and cleaner ways to interact with information.
These strategies with simpler designs are important, now more than ever, to help social
organizations, such as Re-Food, improve their work and show to communities that they impact directly
and indirectly the importance of eco-sustainability and solidarity projects.
This project proposes to build an information system capable of structuring data from Re-Food
resources, create faster and more efficient solutions to improve the centers’ management and show
to the rest of the community the impact this organization, and the ones alike have on society
Procedure-Aware Pretraining for Instructional Video Understanding
Our goal is to learn a video representation that is useful for downstream
procedure understanding tasks in instructional videos. Due to the small amount
of available annotations, a key challenge in procedure understanding is to be
able to extract from unlabeled videos the procedural knowledge such as the
identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or
the potential next steps given partial progress in its execution. Our main
insight is that instructional videos depict sequences of steps that repeat
between instances of the same or different tasks, and that this structure can
be well represented by a Procedural Knowledge Graph (PKG), where nodes are
discrete steps and edges connect steps that occur sequentially in the
instructional activities. This graph can then be used to generate pseudo labels
to train a video representation that encodes the procedural knowledge in a more
accessible form to generalize to multiple procedure understanding tasks. We
build a PKG by combining information from a text-based procedural knowledge
database and an unlabeled instructional video corpus and then use it to
generate training pseudo labels with four novel pre-training objectives. We
call this PKG-based pre-training procedure and the resulting model Paprika,
Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We
evaluate Paprika on COIN and CrossTask for procedure understanding tasks such
as task recognition, step recognition, and step forecasting. Paprika yields a
video representation that improves over the state of the art: up to 11.23%
gains in accuracy in 12 evaluation settings. Implementation is available at
https://github.com/salesforce/paprika.Comment: CVPR 202
PreFair: Privately Generating Justifiably Fair Synthetic Data
When a database is protected by Differential Privacy (DP), its usability is
limited in scope. In this scenario, generating a synthetic version of the data
that mimics the properties of the private data allows users to perform any
operation on the synthetic data, while maintaining the privacy of the original
data. Therefore, multiple works have been devoted to devising systems for DP
synthetic data generation. However, such systems may preserve or even magnify
properties of the data that make it unfair, endering the synthetic data unfit
for use. In this work, we present PreFair, a system that allows for DP fair
synthetic data generation. PreFair extends the state-of-the-art DP data
generation mechanisms by incorporating a causal fairness criterion that ensures
fair synthetic data. We adapt the notion of justifiable fairness to fit the
synthetic data generation scenario. We further study the problem of generating
DP fair synthetic data, showing its intractability and designing algorithms
that are optimal under certain assumptions. We also provide an extensive
experimental evaluation, showing that PreFair generates synthetic data that is
significantly fairer than the data generated by leading DP data generation
mechanisms, while remaining faithful to the private data.Comment: 15 pages, 11 figure
Categories and foundational ontology: A medieval tutorial
Foundational ontologies, central constructs in ontological investigations and engineering alike, are based on ontological categories. Firstly proposed by Aristotle as the very ur- elements from which the whole of reality can be derived, they are not easy to identify, let alone partition and/or hierarchize; in particular, the question of their number poses serious challenges. The late medieval philosopher Dietrich of Freiberg wrote around 1286 a tutorial that can help us today with this exceedingly difficult task. In this paper, I discuss ontological categories and their importance for foundational ontologies from both the contemporary perspective and the original Aristotelian viewpoint, I provide the translation from the Latin into English of Dietrich's De origine II with an introductory elaboration, and I extract a foundational ontology–that is in fact a single-category one–from this text rooted in Dietrich's specification of types of subjecthood and his conception of intentionality as causal operation
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