301,613 research outputs found
Energy efficiency in discrete-manufacturing systems: insights, trends, and control strategies
Since the depletion of fossil energy sources, rising energy prices, and governmental regulation restrictions, the current manufacturing industry is shifting towards more efficient and sustainable systems. This transformation has promoted the identification of energy saving opportunities and the development of new technologies and strategies oriented to improve the energy efficiency of such systems. This paper outlines and discusses most of the research reported during the last decade regarding energy efficiency in manufacturing systems, the current technologies and strategies to improve that efficiency, identifying and remarking those related to the design of management/control strategies. Based on this fact, this paper aims to provide a review of strategies for reducing energy consumption and optimizing the use of resources within a plant into the context of discrete manufacturing. The review performed concerning the current context of manufacturing systems, control systems implemented, and their transformation towards Industry 4.0 might be useful in both the academic and industrial dimension to identify trends and critical points and suggest further research lines.Peer ReviewedPreprin
Mitigating Gender Bias in Machine Learning Data Sets
Artificial Intelligence has the capacity to amplify and perpetuate societal
biases and presents profound ethical implications for society. Gender bias has
been identified in the context of employment advertising and recruitment tools,
due to their reliance on underlying language processing and recommendation
algorithms. Attempts to address such issues have involved testing learned
associations, integrating concepts of fairness to machine learning and
performing more rigorous analysis of training data. Mitigating bias when
algorithms are trained on textual data is particularly challenging given the
complex way gender ideology is embedded in language. This paper proposes a
framework for the identification of gender bias in training data for machine
learning.The work draws upon gender theory and sociolinguistics to
systematically indicate levels of bias in textual training data and associated
neural word embedding models, thus highlighting pathways for both removing bias
from training data and critically assessing its impact.Comment: 10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as
part of the ECIR Conference) - http://bias.disim.univaq.i
Does interactivity require multimedia? The case of SAKI
SAKI is a self‐adaptive touch‐typing tutor with a pedigree dating back to the mid‐1950s. Even in its most recent form it eschews the temptation to present itself with the trimmings now commonly associated with microcomputer products. This paper argues that while the absence of such features may be a limiting factor in the commercial success of the program, SAKI is nevertheless a prime example of the way in which a computer can successfully react to and interact with a user, and indeed one which would actually lose educational value if it were to undergo an interface‐lift
Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges
Human-swarm interaction (HSI) involves a number of human factors impacting
human behaviour throughout the interaction. As the technologies used within HSI
advance, it is more tempting to increase the level of swarm autonomy within the
interaction to reduce the workload on humans. Yet, the prospective negative
effects of high levels of autonomy on human situational awareness can hinder
this process. Flexible autonomy aims at trading-off these effects by changing
the level of autonomy within the interaction when required; with
mixed-initiatives combining human preferences and automation's recommendations
to select an appropriate level of autonomy at a certain point of time. However,
the effective implementation of mixed-initiative systems raises fundamental
questions on how to combine human preferences and automation recommendations,
how to realise the selected level of autonomy, and what the future impacts on
the cognitive states of a human are. We explore open challenges that hamper the
process of developing effective flexible autonomy. We then highlight the
potential benefits of using system modelling techniques in HSI by illustrating
how they provide HSI designers with an opportunity to evaluate different
strategies for assessing the state of the mission and for adapting the level of
autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling
Conference, Canberra, Australi
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
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