19,535 research outputs found
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
Mining data streams is one of the main studies in machine learning area due
to its application in many knowledge areas. One of the major challenges on
mining data streams is concept drift, which requires the learner to discard the
current concept and adapt to a new one. Ensemble-based drift detection
algorithms have been used successfully to the classification task but usually
maintain a fixed size ensemble of learners running the risk of needlessly
spending processing time and memory. In this paper we present improvements to
the Scale-free Network Regressor (SFNR), a dynamic ensemble-based method for
regression that employs social networks theory. In order to detect concept
drifts SFNR uses the Adaptive Window (ADWIN) algorithm. Results show
improvements in accuracy, especially in concept drift situations and better
performance compared to other state-of-the-art algorithms in both real and
synthetic data
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
An exploration of the language within Ofsted reports and their influence on primary school performance in mathematics: a mixed methods critical discourse analysis
This thesis contributes to the understanding of the language of Ofsted reports, their similarity to one another and associations between different terms used within âareas for improvementâ sections and subsequent outcomes for pupils. The research responds to concerns from serving headteachers that Ofsted reports are overly similar, do not capture the unique story of their school, and are unhelpful for improvement. In seeking to answer âhow similar are
Ofsted reportsâ the study uses two tools, a plagiarism detection software (Turnitin) and a discourse analysis tool (NVivo) to identify trends within and across a large corpus of reports.
The approach is based on critical discourse analysis (Van Dijk, 2009; Fairclough, 1989) but shaped in the form of practitioner enquiry seeking power in the form of impact on pupils and practitioners, rather than a more traditional, sociological application of the method.
The research found that in 2017, primary school section 5 Ofsted reports had more than half of their content exactly duplicated within other primary school inspection reports published that same year. Discourse analysis showed the quality assurance process overrode variables such as inspector designation, gender, or team size, leading to three distinct patterns of duplication: block duplication, self-referencing, and template writing. The most unique part of a report was found to be the âarea for improvementâ section, which was tracked to externally verified outcomes for pupils using terms linked to âmathematicsâ. Those
required to improve mathematics in their areas for improvement improved progress and attainment in mathematics significantly more than national rates. These findings indicate that there was a positive correlation between the inspection reporting process and a beneficial impact on pupil outcomes in mathematics, and that the significant similarity of one report to another had no bearing on the usefulness of the report for school improvement purposes
within this corpus
Sensitivity analysis for ReaxFF reparameterization using the Hilbert-Schmidt independence criterion
We apply a global sensitivity method, the Hilbert-Schmidt independence
criterion (HSIC), to the reparameterization of a Zn/S/H ReaxFF force field to
identify the most appropriate parameters for reparameterization. Parameter
selection remains a challenge in this context as high dimensional optimizations
are prone to overfitting and take a long time, but selecting too few parameters
leads to poor quality force fields. We show that the HSIC correctly and quickly
identifies the most sensitive parameters, and that optimizations done using a
small number of sensitive parameters outperform those done using a higher
dimensional reasonable-user parameter selection. Optimizations using only
sensitive parameters: 1) converge faster, 2) have loss values comparable to
those found with the naive selection, 3) have similar accuracy in validation
tests, and 4) do not suffer from problems of overfitting. We demonstrate that
an HSIC global sensitivity is a cheap optimization pre-processing step that has
both qualitative and quantitative benefits which can substantially simplify and
speedup ReaxFF reparameterizations.Comment: author accepted manuscrip
Advancing Model Pruning via Bi-level Optimization
The deployment constraints in practical applications necessitate the pruning
of large-scale deep learning models, i.e., promoting their weight sparsity. As
illustrated by the Lottery Ticket Hypothesis (LTH), pruning also has the
potential of improving their generalization ability. At the core of LTH,
iterative magnitude pruning (IMP) is the predominant pruning method to
successfully find 'winning tickets'. Yet, the computation cost of IMP grows
prohibitively as the targeted pruning ratio increases. To reduce the
computation overhead, various efficient 'one-shot' pruning methods have been
developed, but these schemes are usually unable to find winning tickets as good
as IMP. This raises the question of how to close the gap between pruning
accuracy and pruning efficiency? To tackle it, we pursue the algorithmic
advancement of model pruning. Specifically, we formulate the pruning problem
from a fresh and novel viewpoint, bi-level optimization (BLO). We show that the
BLO interpretation provides a technically-grounded optimization base for an
efficient implementation of the pruning-retraining learning paradigm used in
IMP. We also show that the proposed bi-level optimization-oriented pruning
method (termed BiP) is a special class of BLO problems with a bi-linear problem
structure. By leveraging such bi-linearity, we theoretically show that BiP can
be solved as easily as first-order optimization, thus inheriting the
computation efficiency. Through extensive experiments on both structured and
unstructured pruning with 5 model architectures and 4 data sets, we demonstrate
that BiP can find better winning tickets than IMP in most cases, and is
computationally as efficient as the one-shot pruning schemes, demonstrating 2-7
times speedup over IMP for the same level of model accuracy and sparsity.Comment: Thirty-sixth Conference on Neural Information Processing Systems
(NeurIPS 2022
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
Kurcuma: a kitchen utensil recognition collection for unsupervised domain adaptation
The use of deep learning makes it possible to achieve extraordinary results in all kinds of tasks related to computer vision. However, this performance is strongly related to the availability of training data and its relationship with the distribution in the eventual application scenario. This question is of vital importance in areas such as robotics, where the targeted environment data are barely available in advance. In this context, domain adaptation (DA) techniques are especially important to building models that deal with new data for which the corresponding label is not available. To promote further research in DA techniques applied to robotics, this work presents Kurcuma (Kitchen Utensil Recognition Collection for Unsupervised doMain Adaptation), an assortment of seven datasets for the classification of kitchen utensilsâa task of relevance in home-assistance robotics and a suitable showcase for DA. Along with the data, we provide a broad description of the main characteristics of the dataset, as well as a baseline using the well-known domain-adversarial training of neural networks approach. The results show the challenge posed by DA on these types of tasks, pointing to the need for new approaches in future work.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was supported by the I+D+i project TED2021-132103A-I00 (DOREMI), funded by MCIN/AEI/10.13039/501100011033. Some of the computing resources were provided by the Generalitat Valenciana and the European Union through the FEDER funding program (IDIFEDER/2020/003). The second author is supported by grant APOSTD/2020/256 from âPrograma I+D+i de la Generalitat Valencianaâ
On the competitive facility location problem with a Bayesian spatial interaction model
The competitive facility location problem arises when businesses plan to enter a new market or expand their presence. We introduce a Bayesian spatial interaction model which provides probabilistic estimates on location-specific revenues and then formulate a mathematical framework to simultaneously identify the location and design of new facilities that maximise revenue. To solve the allocation optimisation problem, we develop a hierarchical search algorithm and associated sampling techniques that explore geographic regions of varying spatial resolution. We demonstrate the approach by producing optimal facility locations and corresponding designs for two large-scale applications in the supermarket and pub sectors of Greater London
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