PhD scholarship within robotic grasping and manipulation
The Faculty of Science and Technology at the Norwegian University of Life Sciences (NMBU) has a vacant 3-year PhD-position related to robotics
The PhD scholarship is part of the project “Robotised cells to obtain efficient meat production for the Norwegian meat industry (MeaTable)”, funded by the Research Council of Norway, with duration: 1 April 2018 – 31 March 2021.
For more information and to apply for the position, see
https://www.jobbnorge.no/ledige-stillinger/stilling/152249/phd-scholarship-within-robotic-grasping-and-manipulation-refno-18-02682
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Pål Johan From, Ph.D.
Professor
Head of Robotics and Control Group
Faculty of Sciences and Technology
Norwegian University of Life Science
Postbox 5003
1432 Ås
Norway
Room: TF 316
tel: +47 67231603
cel: +47 90690932
e-mail: pafr@nmbu.no
www.nmbu.no/roboticsandcontrol
✔️ @ApplyTime
The Faculty of Science and Technology at the Norwegian University of Life Sciences (NMBU) has a vacant 3-year PhD-position related to robotics
The PhD scholarship is part of the project “Robotised cells to obtain efficient meat production for the Norwegian meat industry (MeaTable)”, funded by the Research Council of Norway, with duration: 1 April 2018 – 31 March 2021.
For more information and to apply for the position, see
https://www.jobbnorge.no/ledige-stillinger/stilling/152249/phd-scholarship-within-robotic-grasping-and-manipulation-refno-18-02682
---
Pål Johan From, Ph.D.
Professor
Head of Robotics and Control Group
Faculty of Sciences and Technology
Norwegian University of Life Science
Postbox 5003
1432 Ås
Norway
Room: TF 316
tel: +47 67231603
cel: +47 90690932
e-mail: pafr@nmbu.no
www.nmbu.no/roboticsandcontrol
✔️ @ApplyTime
Jobbnorge.no
PhD scholarship within robotic grasping and manipulation – ref.no. 18/02682 (152249) | Norwegian University of Life Sciences (NMBU)
Stillingstittel: PhD scholarship within robotic grasping and manipulation – ref.no. 18/02682 (152249), Arbeidsgiver: Norwegian University of Life Sciences (NMBU), Søknadsfrist: 5. juni 2018
Call for papers: IJCAI Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge
http://www.iiia.csic.es/LR2018/
Submission deadline: May 15
Knowledge representation and reasoning (KRR), on the one hand, and machine learning (ML), on the other hand, have largely been developed as independent research trends in artificial intelligence (AI). Human reasoning, however, is often based on an intricate combination of processes that are related to learning (e.g. induction or extrapolation) and processes that are closer to deductive reasoning. Similarly, we can expect that progress in AI will increasingly need to rely on hybrid approaches that combine the explainability and teachability of KRR methods with the robustness and data-driven nature of ML methods. The ambitious aim of truly integrating reasoning and learning beyond one-way linkage raises many new questions, which this workshop hopes to explore. Beyond a study of the underlying principles, this workshop will also focus on applications, with a particular emphasis on the use of spatial and temporal knowledge in everyday tasks. The workshop will serve as a forum for researchers from different fields (including Automated Theorem Proving, Cognitive Computing, Cognitive Robotics, Commonsense Reasoning, Constraint Solving, Logic, Mathematics, Machine Learning, Natural Language Processing, Theoretical Computer Science, Qualitative Reasoning) to discuss open problems, methodology, and recent advancements in the field. It also provides a forum for early career researchers to present their current work and to build up networks. We invite technical contributions mixing or linking advanced ideas from knowledge representation and reasoning with ideas from machine learning or data mining.
Workshop organisers:
Vaishak Belle, University of Edinburgh, UK
Lluís Godo, IIIA - CSIC, Barcelona, Spain
Henri Prade, IRIT, Toulouse, France
Jochen Renz, The Australian National University, Australia
Steven Schockaert, University of Cardiff, UK
Ute Schmid, University of Bamberg, Germany
Diedrich Wolter, University of Bamberg, German
✔️ @ApplyTime
http://www.iiia.csic.es/LR2018/
Submission deadline: May 15
Knowledge representation and reasoning (KRR), on the one hand, and machine learning (ML), on the other hand, have largely been developed as independent research trends in artificial intelligence (AI). Human reasoning, however, is often based on an intricate combination of processes that are related to learning (e.g. induction or extrapolation) and processes that are closer to deductive reasoning. Similarly, we can expect that progress in AI will increasingly need to rely on hybrid approaches that combine the explainability and teachability of KRR methods with the robustness and data-driven nature of ML methods. The ambitious aim of truly integrating reasoning and learning beyond one-way linkage raises many new questions, which this workshop hopes to explore. Beyond a study of the underlying principles, this workshop will also focus on applications, with a particular emphasis on the use of spatial and temporal knowledge in everyday tasks. The workshop will serve as a forum for researchers from different fields (including Automated Theorem Proving, Cognitive Computing, Cognitive Robotics, Commonsense Reasoning, Constraint Solving, Logic, Mathematics, Machine Learning, Natural Language Processing, Theoretical Computer Science, Qualitative Reasoning) to discuss open problems, methodology, and recent advancements in the field. It also provides a forum for early career researchers to present their current work and to build up networks. We invite technical contributions mixing or linking advanced ideas from knowledge representation and reasoning with ideas from machine learning or data mining.
Workshop organisers:
Vaishak Belle, University of Edinburgh, UK
Lluís Godo, IIIA - CSIC, Barcelona, Spain
Henri Prade, IRIT, Toulouse, France
Jochen Renz, The Australian National University, Australia
Steven Schockaert, University of Cardiff, UK
Ute Schmid, University of Bamberg, Germany
Diedrich Wolter, University of Bamberg, German
✔️ @ApplyTime
28th International Conference on Inductive Logic Programming
September 2nd - 4th 2018, Ferrara, Italy
http://ilp2018.unife.it/
✔️ @ApplyTime
September 2nd - 4th 2018, Ferrara, Italy
http://ilp2018.unife.it/
✔️ @ApplyTime
Dear All,
Greetings!!
We cordially invite you to the "25th Edition of International Conference for Ophthalmologists" scheduled to be held on September 10-11, 2018 in Copenhagen, Denmark. The theme of the conference is "Vision Saviours- Protecting Eyes, Empowering Sight".
For more information about the conference visit: Ophthalmology Conferences
Your expertise and experience in this field of work will be an excellent addition to our programme on Ophthalmologists 2018 as many of our visitors look forward to hearing and learning from your work. We want you to be a part of this conference and lead us to the road of success.
We look forward for your confirmed presence at the Copenhagen, Denmark.
Yours sincerely,
Megan Harper| Program Director
ophthalmologists@eurosciconconferences.com
(+44) 020 3807 3712
Bloomsbury Way
Lower Ground Floor
London, United Kingdom
WC1A 2SE.
✔️ @ApplyTime
Greetings!!
We cordially invite you to the "25th Edition of International Conference for Ophthalmologists" scheduled to be held on September 10-11, 2018 in Copenhagen, Denmark. The theme of the conference is "Vision Saviours- Protecting Eyes, Empowering Sight".
For more information about the conference visit: Ophthalmology Conferences
Your expertise and experience in this field of work will be an excellent addition to our programme on Ophthalmologists 2018 as many of our visitors look forward to hearing and learning from your work. We want you to be a part of this conference and lead us to the road of success.
We look forward for your confirmed presence at the Copenhagen, Denmark.
Yours sincerely,
Megan Harper| Program Director
ophthalmologists@eurosciconconferences.com
(+44) 020 3807 3712
Bloomsbury Way
Lower Ground Floor
London, United Kingdom
WC1A 2SE.
✔️ @ApplyTime
Hello everyone,
We are seeking to hire a postdoctoral fellow to work in the multi-disciplinary research area of machine learning and health informatics. The postdoctoral fellow will primarily work on a project on detecting aggression in people living with dementia using wearable and ambient sensors.
The position will be based at Toronto Rehabilitation Institute, University Health Network (TRI-UHN) and University of Toronto (UT) and will be co-supervised by Dr. Shehroz Khan, Scientist at TRI-UHN and Dr. Alex Mihailidis, Professor at UT.
Some information on the project is available here - https://www.researchgate.net/project/Agitation-and-Aggression-Detection-in-Older-Adults-with-Dementia
More details of the job is available here - http://individual.utoronto.ca/shehroz/files/AgitationPrediction_postdoc.pdf. Please follow the instructions to apply.
regards
Dr. Shehroz Khan
✔ @ApplyTime
We are seeking to hire a postdoctoral fellow to work in the multi-disciplinary research area of machine learning and health informatics. The postdoctoral fellow will primarily work on a project on detecting aggression in people living with dementia using wearable and ambient sensors.
The position will be based at Toronto Rehabilitation Institute, University Health Network (TRI-UHN) and University of Toronto (UT) and will be co-supervised by Dr. Shehroz Khan, Scientist at TRI-UHN and Dr. Alex Mihailidis, Professor at UT.
Some information on the project is available here - https://www.researchgate.net/project/Agitation-and-Aggression-Detection-in-Older-Adults-with-Dementia
More details of the job is available here - http://individual.utoronto.ca/shehroz/files/AgitationPrediction_postdoc.pdf. Please follow the instructions to apply.
regards
Dr. Shehroz Khan
✔ @ApplyTime
ResearchGate
Agitation and Aggression Detection in Older Adults with Dementia
Agitation and Aggression Detection in Older Adults with Dementia | * We will use wearable devices to capture motion and physiological data and other ambient sensors such as motion and contact sensors for recording activities.
* We will use camera recordings…
* We will use camera recordings…
We are looking for a postdoctoral researcher with expertise in machine learning to work on an exciting collaboration project between Prof. S. Kaski’s team in Finnish Center for Artificial Intelligence FCAI at Aalto University and Janssen Pharmaceutica. The exciting research problem is to learn to infer toxicity of chemicals based on the chemical structure, and the great opportunity is that we have unique data for the learning. This is an outstanding opportunity to both develop new probabilistic machine learning methods and to gain necessary skills to place yourself for success both in the pharmaceutical industry as well as in academia. Janssen Pharmaceutica and FCAI support and expect publication of the outcome of this work in top peer-reviewed journals and scientific conferences.
Deadline: May 28, 2018
Full details: http://www.aalto.fi/en/about/careers/jobs/view/1854/.
✔ @ApplyTime
Deadline: May 28, 2018
Full details: http://www.aalto.fi/en/about/careers/jobs/view/1854/.
✔ @ApplyTime
www.aalto.fi
Careers | Aalto University
Looking for a job at Aalto University? See our open positions.
Forwarded from Apply Time
5th International Workshop on Outlier Detection and Denoscription (ODD 2018)
(co-located with ACM SIGKDD 2018)
August 20, 2018
London, UK
Abstract Submission: May 8, 2018
Full Submission Deadline: May 15, 2018
Details at http://www.andrew.cmu.edu/user/lakoglu/odd/index.html
✔ @ApplyTime
(co-located with ACM SIGKDD 2018)
August 20, 2018
London, UK
Abstract Submission: May 8, 2018
Full Submission Deadline: May 15, 2018
Details at http://www.andrew.cmu.edu/user/lakoglu/odd/index.html
✔ @ApplyTime
PhD position in Epigenetics and Reproduction
https://apply.refline.ch/845721/6205/pub/1/index.html
✔ @ApplyTime
https://apply.refline.ch/845721/6205/pub/1/index.html
✔ @ApplyTime
PhD position: fuzzy rough set models for machine learning and sentiment analysis, University of Ghent, Belgium
https://goo.gl/azcfGh
✔ @ApplyTime
https://goo.gl/azcfGh
✔ @ApplyTime
PhD Student/post-doctoral position in bioinformatics for the Single-cell trannoscriptomics of cancer research group; University of Helsinki, Finland
https://goo.gl/a7paX5
✔ @ApplyTime
https://goo.gl/a7paX5
✔ @ApplyTime
PhD in Modelling and Optimisation of spatio-temporal patterns of flows in the urban Food-Water-Energy nexus, Delft University of Technology, Netherlands
https://goo.gl/t4uj6E
✔ @ApplyTime
https://goo.gl/t4uj6E
✔ @ApplyTime
Forwarded from Apply Time
#Phrase
📋 فهرستی از #واژههایی که در این کانال با آنها تاکنون مواجه شده، یا در ادامه خواهید شد:
📝 1- Scholarship, Fund, Grant, Bursary:
🔎بورسیهی تحصیلی یا همان کمک هزینهی تحصیلی (خواه مبلغی که کل هزینههای شما را پوشش میدهد، و خواه بخشی از آن را).
📝 2- Fully-Funded Scholarship:
🔎 بورسیهی تحصیلیای که کل هزینهی شما را پوشش میدهد.
📝 3- Partially-Funded Scholarship:
🔎 بورسیهی تحصیلیای که بخشی از هزینهها را پوشش میدهد.
📝 4- Tuition fee:
🔎 شهریهی دانشگاه.
که لزوما همهی دانشگاهها شهریه ندارند. برای مثال عموماً دانشگاههای آلمان تقریبا بدون شهریه هستند. در حالیکه اکثریت دانشگاههای انگلستان شهریه دارند.
📝 5- Costs of living:
🔎هزینههای زندگی.
📝 6- Tuition fee waiver award:
🔎 اشاره به بورسیهای تحصیلیای دارد که جهت پرداخت بابت کل یا بخشی از شهریه میباشد. که معمولا مستقیم به حساب دانشگاه واریز میشود نه به حساب دانشجو.
📝 7- Stipend:
🔎 حقوق. مقرری.
📝 8- Position:
🔎 منظور یک موقعیت تحصیلی است. به عنوان مثال:
PhD position
اشاره به یک موقعیت تحصیلی در مقطع دکتری دارد. یا
Faculty position
اشاره به جایگاه هیئت علمی دارد.
📝 9- Open PhD position:
🔎 منظور این است که استاد، آزمایشگاه، دپارتمان (دانشکده)، یا دانشگاه مورد نظر جایگاههایی دارند برای استخدام دانشجویان دکتری.
📝 10- Inquiry:
🔎 پرس و جو کردن.
به عنوان مثال وقتی شما به استادی ایمیل میزنید و از او جویا میشوید که آیا میتواند برای ترم آتی دانشجو بگیرد یا خیر؟؛ شما دارید از او پرس و جو میکنید.
📝 11- Recruit:
🔎 استخدام کردن.
به عنوان مثال، استاد یا دانشگاهی شما را برای مقطع دکتری استخدام میکند.
📝 12- Admission:
🔎 گروه مدیریتی یک دانشکده (یا دانشگاه)، مثلا گروه دانشکده برق: کسانی که مدارک شما را بررسی میکنند تا در صورت مناسب بودن به شما پاسخ مثبت دهند.
📝 13- Application:
🔎 منظور درخواست شما و مجموعه مدارکی است که شما برای استاد، آزمایشگاه، دپارتمان یا ادمیشن ارسال میکنید تا بررسی شوند و به شما پاسخی دهند.
📝 14- Offer:
🔎 پیشنهاد دادن.
وقتی دانشگاهی اپلیکیشن شما را مثبت ارزیابی کرد، به شما آفر یا اصطلاحا ادمیشن میدهد.
📝 15- BA, BS or Undergraduate
🔎دورهی لیسانس
📝 16- MA, MS or Graduate
🔎 دورهی فوقلیسانس
📝 17- PhD or Graduate
🔎 دورهی دکتری
📝 18- Postdoc
🔎 دورهی پسادکتری
📝 19- Funded
🔎 نشان میدهد که پوزیشن مورد نظر همراه با فاند است. خواه فاند کامل خواه ناکامل.
البته تمامی پوزیشنهای معرفی شده در کانال با فاند همراه هستند.
📝 20- Apply Time:
🔎 احتمالا بهترین همراه شما در امر پذیرش و اعزام به خارج از کشور.
✔️ @ApplyTime
📋 فهرستی از #واژههایی که در این کانال با آنها تاکنون مواجه شده، یا در ادامه خواهید شد:
📝 1- Scholarship, Fund, Grant, Bursary:
🔎بورسیهی تحصیلی یا همان کمک هزینهی تحصیلی (خواه مبلغی که کل هزینههای شما را پوشش میدهد، و خواه بخشی از آن را).
📝 2- Fully-Funded Scholarship:
🔎 بورسیهی تحصیلیای که کل هزینهی شما را پوشش میدهد.
📝 3- Partially-Funded Scholarship:
🔎 بورسیهی تحصیلیای که بخشی از هزینهها را پوشش میدهد.
📝 4- Tuition fee:
🔎 شهریهی دانشگاه.
که لزوما همهی دانشگاهها شهریه ندارند. برای مثال عموماً دانشگاههای آلمان تقریبا بدون شهریه هستند. در حالیکه اکثریت دانشگاههای انگلستان شهریه دارند.
📝 5- Costs of living:
🔎هزینههای زندگی.
📝 6- Tuition fee waiver award:
🔎 اشاره به بورسیهای تحصیلیای دارد که جهت پرداخت بابت کل یا بخشی از شهریه میباشد. که معمولا مستقیم به حساب دانشگاه واریز میشود نه به حساب دانشجو.
📝 7- Stipend:
🔎 حقوق. مقرری.
📝 8- Position:
🔎 منظور یک موقعیت تحصیلی است. به عنوان مثال:
PhD position
اشاره به یک موقعیت تحصیلی در مقطع دکتری دارد. یا
Faculty position
اشاره به جایگاه هیئت علمی دارد.
📝 9- Open PhD position:
🔎 منظور این است که استاد، آزمایشگاه، دپارتمان (دانشکده)، یا دانشگاه مورد نظر جایگاههایی دارند برای استخدام دانشجویان دکتری.
📝 10- Inquiry:
🔎 پرس و جو کردن.
به عنوان مثال وقتی شما به استادی ایمیل میزنید و از او جویا میشوید که آیا میتواند برای ترم آتی دانشجو بگیرد یا خیر؟؛ شما دارید از او پرس و جو میکنید.
📝 11- Recruit:
🔎 استخدام کردن.
به عنوان مثال، استاد یا دانشگاهی شما را برای مقطع دکتری استخدام میکند.
📝 12- Admission:
🔎 گروه مدیریتی یک دانشکده (یا دانشگاه)، مثلا گروه دانشکده برق: کسانی که مدارک شما را بررسی میکنند تا در صورت مناسب بودن به شما پاسخ مثبت دهند.
📝 13- Application:
🔎 منظور درخواست شما و مجموعه مدارکی است که شما برای استاد، آزمایشگاه، دپارتمان یا ادمیشن ارسال میکنید تا بررسی شوند و به شما پاسخی دهند.
📝 14- Offer:
🔎 پیشنهاد دادن.
وقتی دانشگاهی اپلیکیشن شما را مثبت ارزیابی کرد، به شما آفر یا اصطلاحا ادمیشن میدهد.
📝 15- BA, BS or Undergraduate
🔎دورهی لیسانس
📝 16- MA, MS or Graduate
🔎 دورهی فوقلیسانس
📝 17- PhD or Graduate
🔎 دورهی دکتری
📝 18- Postdoc
🔎 دورهی پسادکتری
📝 19- Funded
🔎 نشان میدهد که پوزیشن مورد نظر همراه با فاند است. خواه فاند کامل خواه ناکامل.
البته تمامی پوزیشنهای معرفی شده در کانال با فاند همراه هستند.
📝 20- Apply Time:
🔎 احتمالا بهترین همراه شما در امر پذیرش و اعزام به خارج از کشور.
✔️ @ApplyTime
Privacy in Machine Learning and Artificial Intelligence (ICML’18 Workshop)
Submission deadline: May 14, 2018 (11pm59 CET)
Notification of acceptance: May 29, 2018
Workshop date: July 13/14/15, 2018, Stockholm
Website: https://pimlai.github.io/pimlai18/
News: We have travel grants available to support students and early career researchers interested in attending the workshop. Application deadline is June 4. See the workshop website for more information.
✔️ @ApplyTime
Submission deadline: May 14, 2018 (11pm59 CET)
Notification of acceptance: May 29, 2018
Workshop date: July 13/14/15, 2018, Stockholm
Website: https://pimlai.github.io/pimlai18/
News: We have travel grants available to support students and early career researchers interested in attending the workshop. Application deadline is June 4. See the workshop website for more information.
✔️ @ApplyTime
Postdoc: with Amos Storkey, School of Informatics, University of Edinburgh.
Deadline: 18-May-2018
Title: Deep Learning Methods for Constrained Environments
Requirements: Machine Learning or related PhD or equivalent.
Further details and to apply:
https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=043488
Informal discussion is very welcome: please email amos+postdoc0518@inf.ed.REMOVE.ac.uk with a CV.
✔️ @ApplyTime
Deadline: 18-May-2018
Title: Deep Learning Methods for Constrained Environments
Requirements: Machine Learning or related PhD or equivalent.
Further details and to apply:
https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=043488
Informal discussion is very welcome: please email amos+postdoc0518@inf.ed.REMOVE.ac.uk with a CV.
✔️ @ApplyTime
CFP (June 30): IEEE TNNLS Special Issue on "Recent Advances in Theory, Methodology and Applications of Imbalanced Learning"
Extended Submission Deadline: June 30, 2018.
Learning from imbalanced/unbalanced data (aka imbalanced learning or class-imbalance learning) is a challenging task faced by practitioners from a wide variety of communities. In the last two decades, researchers from various disciplines including data mining, machine learning, pattern recognition and statistics have intensively investigated this theme. However, as pointed out in the 2013 book “Imbalanced Learning: Foundations, Algorithms, and Applications” collectively authored by experts in this field, many if not the most approaches to imbalanced learning are very heuristic and ad hoc, and thus many open questions remain there: “What is the assurance that algorithms specifically designed for imbalanced learning could really help, and how and why?”; “Is there a way we could develop a theoretical guidance on which based learning algorithm is most appropriate for a particular type of imbalanced data?”; “What is the relationship between data-imbalanced ratio and learning model complexity?”, for example. Moreover, in recent years the datasets that practitioners are concerned have grown increasingly rapidly and complexly; many new applications, and thus new types of data and new learning paradigms, have emerged. Therefore, this special issue aims to call for the state-of-the-art research work in the theory, methodology and applications of imbalanced learning, and aims to demonstrate the recent efforts made by the relevant researchers from a wide range of disciplines.
We welcome all the original work on topics regarding new theory, methodology and applications of imbalanced learning, including but not limited to:
* Deep learning for large-scale imbalanced data
* Representation learning for imbalanced data
* Reinforcement learning for imbalanced data
* Active learning and passive learning for imbalanced data
* Transfer learning and concept drift for imbalanced data
* Imbalanced learning in non-stationary environments
* Online learning and incremental learning for imbalanced data
* Statistical modelling for (non-Gaussian) imbalanced data
* Statistical machine learning for imbalanced data
* Discriminative learning and generative learning for imbalanced data
* Similarity/metric learning for imbalanced data
* Ensemble learning for imbalanced data
* Related learning problems: one-class classification, novelty/outlier/anomaly detection
* Theoretical analysis of models and algorithms for imbalanced learning
* New evaluation metrics for imbalanced learning
* New applications of imbalanced learning: 1) Object detection, classification, recognition; 2) Image retrieval, segmentation, understanding; 3) Speech recognition, synthesis, anti-spoofing; 4) Document retrieval, categorization, topic model; 5) Biomedical signal processing, medical image analysis, bioinformatics; 6) Fault detection/diagnosis, fraud detection, cyber-security; and 7) Other related novel applications
IMPORTANT DATES
30 June 2018 -- Deadline for manunoscript submission
31 August 2018 -- Notification of authors
31 October 2018 -- Deadline for submission of revised manunoscripts
31 December 2018 -- Final decision of acceptance
February 2019 -- Tentative publication date
SUBMISSION INSTRUCTIONS
1. Read the information for Authors at http://cis.ieee.org/tnnls.
2. Submit your manunoscript at the TNNLS webpage (http://mc.manunoscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manunoscript and in the cover letter that the manunoscript is submitted to this special issue. Send an email to the leading editor Dr. Jing-Hao Xue (jinghao.xue@ucl.ac.uk) with subject “TNNLS special issue submission” to notify about your submission.
3. Early submissions are welcome. We will start the review process as soon as we receive your contributions.
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Extended Submission Deadline: June 30, 2018.
Learning from imbalanced/unbalanced data (aka imbalanced learning or class-imbalance learning) is a challenging task faced by practitioners from a wide variety of communities. In the last two decades, researchers from various disciplines including data mining, machine learning, pattern recognition and statistics have intensively investigated this theme. However, as pointed out in the 2013 book “Imbalanced Learning: Foundations, Algorithms, and Applications” collectively authored by experts in this field, many if not the most approaches to imbalanced learning are very heuristic and ad hoc, and thus many open questions remain there: “What is the assurance that algorithms specifically designed for imbalanced learning could really help, and how and why?”; “Is there a way we could develop a theoretical guidance on which based learning algorithm is most appropriate for a particular type of imbalanced data?”; “What is the relationship between data-imbalanced ratio and learning model complexity?”, for example. Moreover, in recent years the datasets that practitioners are concerned have grown increasingly rapidly and complexly; many new applications, and thus new types of data and new learning paradigms, have emerged. Therefore, this special issue aims to call for the state-of-the-art research work in the theory, methodology and applications of imbalanced learning, and aims to demonstrate the recent efforts made by the relevant researchers from a wide range of disciplines.
We welcome all the original work on topics regarding new theory, methodology and applications of imbalanced learning, including but not limited to:
* Deep learning for large-scale imbalanced data
* Representation learning for imbalanced data
* Reinforcement learning for imbalanced data
* Active learning and passive learning for imbalanced data
* Transfer learning and concept drift for imbalanced data
* Imbalanced learning in non-stationary environments
* Online learning and incremental learning for imbalanced data
* Statistical modelling for (non-Gaussian) imbalanced data
* Statistical machine learning for imbalanced data
* Discriminative learning and generative learning for imbalanced data
* Similarity/metric learning for imbalanced data
* Ensemble learning for imbalanced data
* Related learning problems: one-class classification, novelty/outlier/anomaly detection
* Theoretical analysis of models and algorithms for imbalanced learning
* New evaluation metrics for imbalanced learning
* New applications of imbalanced learning: 1) Object detection, classification, recognition; 2) Image retrieval, segmentation, understanding; 3) Speech recognition, synthesis, anti-spoofing; 4) Document retrieval, categorization, topic model; 5) Biomedical signal processing, medical image analysis, bioinformatics; 6) Fault detection/diagnosis, fraud detection, cyber-security; and 7) Other related novel applications
IMPORTANT DATES
30 June 2018 -- Deadline for manunoscript submission
31 August 2018 -- Notification of authors
31 October 2018 -- Deadline for submission of revised manunoscripts
31 December 2018 -- Final decision of acceptance
February 2019 -- Tentative publication date
SUBMISSION INSTRUCTIONS
1. Read the information for Authors at http://cis.ieee.org/tnnls.
2. Submit your manunoscript at the TNNLS webpage (http://mc.manunoscriptcentral.com/tnnls) and follow the submission procedure. Please, clearly indicate on the first page of the manunoscript and in the cover letter that the manunoscript is submitted to this special issue. Send an email to the leading editor Dr. Jing-Hao Xue (jinghao.xue@ucl.ac.uk) with subject “TNNLS special issue submission” to notify about your submission.
3. Early submissions are welcome. We will start the review process as soon as we receive your contributions.
✔ @ApplyTime
cis.ieee.org
Information for Authors - IEEE Computational Intelligence Society
From its institution as the Neural Networks Council in the early 1990s, the IEEE Computational Intelligence Society has rapidly grown into a robust community with a vision for addressing real-world issues with biologically-motivated computational paradigms.…
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📖 معرفی چند کورس آنلاین زبان انگلیسی، که احتمالا برای شما مفید باشد. (ویدئوهای هر کلاس، قابل دانلود بوده، و همهی کورسها رایگان هستند): 👈
🌐 http://applytime.ir/learn-english-online-free-different-levels-different-skills-different-certifications/
❓کدام یک از موارد فوق را انتخاب کنم؟
💡تفاوتی ندارد. بهتر است بجای زمانی که روی تردیدتان برای انتخاب یکی از این کورسها میگذارید، هر چه سریعتر یکی از آنها را شروع کنید.
✳️ همچنین شما میتوانید در کلاسهای آنلاین و یا حضوری یادگیری زبان اپلایتایم (انگلیسی، فرانسوی، آلمانی) نیز شرکت کنید. در صورت تمایل با ما در ارتباط باشید.
🌈 پل اولیهی ارتباط با ما:
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#Course #TOEFL #IELTS #TESOL #Essay #Conversation #English #Writing #Listening #Grammar #ApplyTime
⚠️ از بین کورسهای زبان انگلیسی معرفی شده در پُست فوق، کورسهای #تافل و #آیلتس توسط تیم اپلایتایم پیشتر دانلود شده و از طریق همین کانال در اختیار شما قرار گرفت.
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Info: https://news.1rj.ru/str/ApplyTime/959
File: https://news.1rj.ru/str/ApplyTime/960
⏹ #IELTS:
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Files:
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https://news.1rj.ru/str/ApplyTime/1028
https://news.1rj.ru/str/ApplyTime/1029
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https://news.1rj.ru/str/ApplyTime/1031
https://news.1rj.ru/str/ApplyTime/1032
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⚠️ از بین کورسهای زبان انگلیسی معرفی شده در پُست فوق، کورسهای #تافل و #آیلتس توسط تیم اپلایتایم پیشتر دانلود شده و از طریق همین کانال در اختیار شما قرار گرفت.
🖇 شما میتوانید از طریق لینکهای زیر، اطلاعات لازم در مورد آن دو کورس را بدست آورده و آنها را دانلود کنید:
⏺ #TOEFL:
Info: https://news.1rj.ru/str/ApplyTime/959
File: https://news.1rj.ru/str/ApplyTime/960
⏹ #IELTS:
Info: https://news.1rj.ru/str/ApplyTime/1026
Files:
https://news.1rj.ru/str/ApplyTime/1027
https://news.1rj.ru/str/ApplyTime/1028
https://news.1rj.ru/str/ApplyTime/1029
https://news.1rj.ru/str/ApplyTime/1030
https://news.1rj.ru/str/ApplyTime/1031
https://news.1rj.ru/str/ApplyTime/1032
✔️ https://news.1rj.ru/str/ApplyTime
It's learning time. Let's go!