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Disco Madness: Walter Gibbons and the Legacy of Turntablism and Remixology

Tim Lawrence
2008 Journal of Popular Music Studies  
As the majors flooded the market with a glut of second-rate disco recordings just as the economy entered a deep recession, disco was critiqued for being superficial, materialistic and irretrievably commercial  ...  "Think (About It)" by Lynn Collins (People, 1972) were played regularly in disco settings, while protagonists from the private party and public discotheque network attest to the way the disco break  ... 
doi:10.1111/j.1533-1598.2008.00162.x fatcat:35mfyxqgrfgdpgmlw46bqqopz4

Preferences, Structure, and Influence: The Engineering of Consent

Witold J. Henisz
2013 Global Strategy Journal  
Simulation analysis highlights important effects from embedding stakeholders within such an interdependent policymaking network.  ...  This process combines insights from agent-based dynamic utility and agent-based dynamic network processes as well as an existing decision framework for stakeholder influence strategies that lacks these  ...  Each actor is both influenced by their peers in the policy network but also seeks to influence them so as to maximize their utility.  ... 
doi:10.1111/j.2042-5805.2013.01064.x fatcat:d55ncmklefcqhi4h442kffxxu4

Learning to see across Domains and Modalities [article]

Fabio Maria Carlucci
2019 arXiv   pre-print
Deep learning has raised hopes and expectations as a general solution for many applications; indeed it has proven effective, but it also showed a strong dependence on large quantities of data.  ...  Transfer learning is a general term, and specific settings have been given specific names: when the learner has only access to unlabeled data from the a target domain and labeled data from a different  ...  problem within an adversarial learning framework or by embedding into deep network specific domain normalization layers.  ... 
arXiv:1902.04992v1 fatcat:p54w7mzkavdedhy5d56hisdl7u

Communication-Efficient Edge AI: Algorithms and Systems [article]

Yuanming Shi, Kai Yang, Tao Jiang, Jun Zhang, Khaled B. Letaief
2020 arXiv   pre-print
This is driven by the explosive growth of data, advances in machine learning (especially deep learning), and easy access to vastly powerful computing resources.  ...  We then introduce communication-efficient techniques, from both algorithmic and system perspectives for training and inference tasks at the network edge.  ...  Zhi Ding from the University of California at Davis for insightful and constructive comments to improve the presentation of this work.  ... 
arXiv:2002.09668v1 fatcat:nhasdzb7t5dt5brs2r7ocdzrnm

Survey of XAI in Digital Pathology [chapter]

Milda Pocevičiūtė, Gabriel Eilertsen, Claes Lundström
2020 Lecture Notes in Computer Science  
Firstly, we give a thorough overview of current XAI techniques of potential relevance for deep learning methods in pathology imaging, and categorise them from three different aspects.  ...  We also connect the technical methods to the specific prerequisites in digital pathology and present findings to guide future research efforts.  ...  Deep learning has been employed for this purpose, both in research [15] and in a commercial application [47] .  ... 
doi:10.1007/978-3-030-50402-1_4 fatcat:lhf7tk4rovewpb6ggdht26vkwa

Extending Knowledge Graphs with Subjective Influence Networks for Personalized Fashion [chapter]

Kurt Bollacker, Natalia Díaz-Rodríguez, Xian Li
2018 Studies in Systems, Decision and Control  
We address this by proposing a supplementary ontological approach in the fashion domain based on subjective influence networks.  ...  We enumerate a set of use cases this approach is intended to address and discuss possible classes of prediction questions and machine learning experiments that could be executed to validate or refute the  ...  Johan Lilius and PhD. Jussi Autere for the financial and human support throughout the Innovation & Entrepreneurship education. From Stitch Fix team we thank Jay B.  ... 
doi:10.1007/978-3-030-00317-3_9 fatcat:r764vdjjgngvzo5mykmuix62ye

A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions [article]

Shulei Ji, Jing Luo, Xinyu Yang
2020 arXiv   pre-print
The utilization of deep learning techniques in generating various contents (such as image, text, etc.) has become a trend.  ...  This paper attempts to provide an overview of various composition tasks under different music generation levels, covering most of the currently popular music generation tasks using deep learning.  ...  Therefore, some researchers have explored the use of word2vec model to learn music vector embedding, and the learned embedding can be employed as the potential input representation of deep learning model  ... 
arXiv:2011.06801v1 fatcat:cixou3d2jzertlcpb7kb5x5ery

An integrative approach to professional development to support college- and career- readiness standards

Katie Pak, Laura M. Desimone, Arianna Parsons
2020 Education Policy Analysis Archives  
States by creatively adapting and integrating the features of this PD framework in order to meet the demands of this mandated educational policy.  ...  We illustrate a revised model for how 70 district officials are conceptualizing these features of PD to support CCR standards-based learning.  ...  of deep learning that affects all members of the organization (Edmondson, 2008) .  ... 
doi:10.14507/epaa.28.4970 fatcat:n4c2ujrcprdytjuwhup5pwj3ge

Automatic Formative Assessment in Computer Science: Guidance to Model-Driven Design

Marina Marchisio, Tiziana Margaria, Matteo Sacchet
2020 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)  
Smoking Interventions Using On the Effectiveness of Random Node Sampling in Influence Maximization on Unknown Graph 613 On Estimating Network Topology from Observed Flow Sets at Measurement Nodes 619  ...  The importance of feedback is one of the most distinctive influences on learning and achievement related to formative assessment and thus object of numerous studies.  ... 
doi:10.1109/compsac48688.2020.00035 dblp:conf/compsac/MarchisioMS20 fatcat:nudzvwviyrcklectabhuyranq4

A survey on data‐efficient algorithms in big data era

Amina Adadi
2021 Journal of Big Data  
Finally, the survey delineates the limitations, discusses research challenges, and suggests future opportunities to advance the research on data-efficiency in machine learning.  ...  AbstractThe leading approaches in Machine Learning are notoriously data-hungry.  ...  (ii) Deep transfer learning, as deep learning becomes a ubiquitous technique, researchers have begun to endow deep models with TL capabilities.  ... 
doi:10.1186/s40537-021-00419-9 fatcat:v4uahsvhlzdldlxqf24bshmja4

Machine Learning for Data-Driven Movement Generation: a Review of the State of the Art [article]

Omid Alemi, Philippe Pasquier
2019 arXiv   pre-print
In this survey, we review and analyze different aspects of building automatic movement generation systems using machine learning techniques and motion capture data.  ...  We cover topics such as high-level movement characterization, training data, features representation, machine learning models, and evaluation methods.  ...  Acknowledgments This work was funded by the Social Sciences and Humanities Research Council of Canada (SSHRC) through the Moving Stories Project, as well as the Natural Sciences and Engineering Research  ... 
arXiv:1903.08356v1 fatcat:wtqawbramvdx3kz6ffgp2sv3ja

6G Technology Overview - One6G White Paper [article]

One6G Association
2022 Zenodo  
6G is supposed to address the demands for consumption of mobile networking services in 2030 and beyond.  ...  ., through the energy efficiency of deployed networks.  ...  These influence the spatial-temporal correlation of the data, which must be considered to maximize energy efficiency.  ... 
doi:10.5281/zenodo.6630705 fatcat:rghtxgovubc5zartbe2qx4ekvm

Content Based Record Label Classification for Electronic Music

Georges Naimeh, Perfecto Herrera, Minz Won
2020 Zenodo  
Neural Network.  ...  The collection has been analyzed with the help of Essentia's library, and the extracted features present various musical criteria such as timbre, rhythm, and tonality.  ...  research, and the features selection and the deep networks architectures might not prove to be the best.  ... 
doi:10.5281/zenodo.4091348 fatcat:c65sjtsc5vfxha47kpgjmrbusm

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Alcardo Alex Barakabitze, Nabajeet Barman, Arslan Ahmad, Saman Zadtootaghaj, Lingfen Sun, Maria G. Martini, Luigi Atzori
2019 IEEE Communications Surveys and Tutorials  
We provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks.  ...  Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative  ...  Using machine learning on top of SDN and NFV, in [257] , a network resource allocation system that provides QoE-aware delivery of media services and autonomous network management to meet the changing  ... 
doi:10.1109/comst.2019.2958784 fatcat:7bgzl5rpmfgedo5e5psz7i3t4a

Assessing Sensory Processing Dysfunction in Adults and Adolescents with Autism Spectrum Disorder: A Scoping Review

Denise DuBois, Erin Lymer, Barbara Gibson, Pushpal Desarkar, Emily Nalder
2017 Brain Sciences  
Method and Analysis: A search of scientific databases and grey literature (professional association and ASD society websites), from January 1987-May 2017, uncovered 4769 articles and 12 clinical guidelines  ...  Sensory reactivity is a diagnostic criterion for Autism Spectrum Disorder (ASD), and has been associated with poorer functional outcomes, behavioral difficulties, and autism severity across the lifespan  ...  In the future, however, brain imaging may be utilized in combination with other assessment methods, to hone in on specific brain networks or structures to target to influence functioning.  ... 
doi:10.3390/brainsci7080108 pmid:28825635 pmcid:PMC5575628 fatcat:g765a7gn7zcifnrooexw3laghm
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