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Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems

Vicente García-Díaz, Jordán Pascual-Espada, Cristina Pelayo G-Bustelo, Juan Manuel Cueva-Lovelle
2015 International Journal of Interactive Multimedia and Artificial Intelligence  
There are different languages, framework and tools to define the data needed to solve machine learning-based problems.  ...  In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based  ...  Internally, definitions are automatically transformed into different Towards a standard-based domain-specific platform to solve machine learning-based problems 5 .  ... 
doi:10.9781/ijimai.2015.351 fatcat:wivgccg44fc5xfbzt5jh6u6ioq

Trinity: A No-Code AI platform for complex spatial datasets [article]

C.V.Krishnakumar Iyer, Feili Hou, Henry Wang, Yonghong Wang, Kay Oh, Swetava Ganguli, Vipul Pandey
2021 arXiv   pre-print
with domain-specific signals and datasets for solving a variety of complex problems on their own.  ...  We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domain experts to experiment  ...  In this paper, we introduce a self-service deep learning platform that uses Convolutional Neural Networks (CNNs) based semantic segmentation to go deep in solving complex problems in the geospatial domain  ... 
arXiv:2106.11756v5 fatcat:iqujkujxundcdlnxle6nfb5eqy

Real World Applications of Machine Learning Techniques over Large Mobile Subscriber Datasets [article]

Jobin Wilson, Chitharanj Kachappilly, Rakesh Mohan, Prateek Kapadia, Arun Soman, Santanu Chaudhury
2015 arXiv   pre-print
service personalization and targeted promotions to a distributed Big Data Analytics platform, capable of performing large scale machine learning and data mining to deliver real time service personalization  ...  In this paper, we describe our journey from a relational database management system (RDBMS) based campaign management solution which allowed data scientists and marketers to use hand-written rules for  ...  Models are expressed as workflows, using a domain specific language (DSL) based on XML, facilitating quick experimentation.  ... 
arXiv:1502.02215v1 fatcat:zgjpne4cu5hn7pecrgvmq437bi

ITS, The End of the World as We Know It: Transitioning AIED into a Service-Oriented Ecosystem

Benjamin D. Nye
2016 International Journal of Artificial Intelligence in Education  
This will represent a move from learning platforms to an ecosystem of interacting learning tools. Such tools will enable new opportunities for both useradaptation and experimentation.  ...  Advanced learning technologies are reaching a new phase of their evolution where they are finally entering mainstream educational contexts, with persistent user bases.  ...  have been developed through research supported by the Office of Naval Research (N00014-12-C-0643, W911NF-04-D-0005), the Army Research Lab (W911NF-14-D-0005, W911NF-12-2-0030), and Advanced Distributed Learning  ... 
doi:10.1007/s40593-016-0098-8 fatcat:4lshgugqinfrpbzd32mayvv3by

Instantly Deployable Expert Knowledge - Networks of Knowledge Engines [article]

Bernhard Bergmair and Thomas Buchegger and Johann Hoffelner and Gerald Schatz and Siegfried Silber and Johannes Klinglmayr
2018 arXiv   pre-print
The ability to acquire these skills is limited for any individual human. Consequently, the capacities to solve problems based on human knowledge in a manual (i.e. mental) way are strongly limited.  ...  Existing developments are linked, including a specific use case in engineering design.  ...  Knowledge helps to solve problems -not one specific problem, but a whole class of problems.  ... 
arXiv:1811.02964v1 fatcat:46tw5xa25jfljbq4bu3rswq44u

Multidisciplinary Problem-Solving Environments for Computational Science [chapter]

Elias N. Houstis, John R. Rice, Naren Ramakrishnan, Tzvetan Drashansky, Sanjiva Weerawarana, Anupam Joshi, C.E. Houstis
1998 Advances in Computers  
We refer to a software realization of muhidisciplinary prototyping throughoUl as a Multidisciplinary Problem Solving Environment (MPSE).  ...  It will allow wholesale reuse of scientific software and provide a natural approach to parallel and distributed problem solving.  ...  C RESEARCH ISSUES TO BE ADDRESSED C.l Domain specific PSEs Even in the early 1960s, scientists had begun to envision problem-solving computing environments nol only powerful enough to solve complex problems  ... 
doi:10.1016/s0065-2458(08)60209-0 fatcat:tshfrtuz2rb3vmmvachxn476gm

The Three Pillars of Machine Programming [article]

Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman Amarasinghe, Joshua B Tenenbaum, Tim Mattson
2021 arXiv   pre-print
Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces.  ...  Adaptation emphasizes advances in the use of ML-based constructs to autonomously evolve software.  ...  The time is ripe for techniques, based on modern machine learning, that learn to map computations onto multiple platforms for a single application.  ... 
arXiv:1803.07244v3 fatcat:omlg3emt3fd7ricvr25erjei4i

Focus Group on Artificial Intelligence for Health [article]

Marcel Salathé, Thomas Wiegand, Markus Wenzel
2018 arXiv   pre-print
Artificial Intelligence (AI) - the phenomenon of machines being able to solve problems that require human intelligence - has in the past decade seen an enormous rise of interest due to significant advances  ...  In particular, it will establish a standardized assessment framework with open benchmarks for the evaluation of AI-based methods for health, such as AI-based diagnosis, triage or treatment decisions.  ...  information, has led to recent stunning advances, with demonstrations of machines achieving human-level competence at solving clearly defined tasks across many domains.  ... 
arXiv:1809.04797v1 fatcat:qf6mnbalqbgyxfnvnpem7xfydi

The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline [article]

Thomas Gabor
2020 arXiv   pre-print
After surveying current approaches to quantum artificial intelligence and relating them to a formal model for machine learning processes, we deduce four major challenges for the future of quantum artificial  ...  be easily combined and exchanged, and (iv) build tools to thoroughly analyze whether observed benefits really stem from quantum properties of the algorithm.  ...  They are a specific hardware platform designed to perform quantum annealing [27] , which is a stochastic optimization algorithm based on adiabatic quantum computing (the exact ideal-condition algorithm  ... 
arXiv:2004.14035v1 fatcat:ii2hj2kspvasdedzxwfkz7wbzy

The three pillars of machine programming

Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman Amarasinghe, Joshua B. Tenenbaum, Tim Mattson
2018 Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages - MAPL 2018  
The time is ripe for techniques, based on modern machine learning, that learn to map computations onto multiple platforms for a single application.  ...  Neural networks with a million real-valued parameters are becoming standard for many applications, whereas the largest problems solved by the SMT-based techniques have on the order of a thousand boolean  ... 
doi:10.1145/3211346.3211355 dblp:conf/pldi/GottschlichSTCR18 fatcat:jfoym7vygbarll7wdwude3crry

Page 3263 of Psychological Abstracts Vol. 83, Issue 8 [page]

1996 Psychological Abstracts  
—Discusses Consultant-2, a system that makes a step toward providing system support for a “pre- and post-pro- cessing” methodology where a cyclic process of experiments with a machine learning (ML) tool  ...  These entities reflect the ways experts organize their problem-solving activities.  ... 

Design of Simulation Competition Platform Based on Cognitive Behavior Modeling

Junjie Zeng, Qi Zhang, Yunxiu Zeng, Long Qin, Mei Yang, Quanjun Yin
2021 Journal of Contemporary Educational Research  
To solve this problem, we propose a simulation competition platform based on cognitive behavior modeling, called TankSim, for undergraduate and graduate students.  ...  This platform aims to cultivate student's team collaboration and innovation capability, and improve their learning motivation.  ...  In order to solve the above-mentioned problems, we propose TankSim, a simulation competition platform based on cognitive behavior modeling.  ... 
doi:10.26689/jcer.v5i8.2457 fatcat:sf3sjvvi4fdhro6iilcqkoth5q

ICDIM 2018 Author Index

2018 2018 Thirteenth International Conference on Digital Information Management (ICDIM)  
interaction mechanisms in blended learning courses involving problem solving e-tivities Vijayakumar, P 74-76 Towards scalable standards for web content usability Vladova, Gergana 156-161 Machine  ...  for Personalized e-Learning with Collaboration Support Krestel, Ralf 63-68 Learning Patent Speak: Investigating Domain-Specific Word Embeddings Kumar, Chiranjeev 274-278 Urdu Text Classification  ... 
doi:10.1109/icdim.2018.8846994 fatcat:f5l5uufqcrbrdb3xdl5id7kabe

Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production

Selim Erol, Andreas Jäger, Philipp Hold, Karl Ott, Wilfried Sihn
2016 Procedia CIRP  
To overcome these burdens, we suggest a Scenario-based Industry 4.0 Learning Factory concept that we are currently planning to implement in Austria's first Industry 4.0 Pilot Factory.  ...  The concept is built upon a tentative competency model for Industry 4.0 and the use of scenarios for problem-oriented learning of future production engineering.  ...  Hence, recent developments show a slow change towards student-centered learning such as problem-based and project-based learning [18] .  ... 
doi:10.1016/j.procir.2016.03.162 fatcat:mgg4zdzoinfbvlwnvxdu3cuawe

Context Aware SmartHealth Cloud Platform for Medical Diagnostics

Sarah Shafqat, Almas Abbasi, Muhammad Naeem, Muhammad Ahsan, Tehmina Amjad, Hafiz Farooq
2018 International Journal of Advanced Computer Science and Applications  
This platform is designed to inherit smartness of unsupervised learning which in turn would keep updating itself under supervised learning by qualified experts.  ...  This analysis lead us to propose a data model for hybrid distributed simulation model for future Context Aware SmartHealth cloud platform for diagnostics.  ...  It is estimated to take huge span of time and expert skills giving us a novel machine learning framework to support our SmartHealth cloud platform. VIII.  ... 
doi:10.14569/ijacsa.2018.090741 fatcat:kcl23226hjh47fmnuituhfndwi
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