1st International Workshop on Deep Learning for Question Answering @ KGSWC 2021

October 6th, 2021 Daniela Lopez de Luise

Please consider submitting a paper for the 1st International workshop on “Deep Learning for Question Answering” which will be held online – November 19-24, 2021.

DLQA: Deep Learning for Question Answering

1st International Workshop, in conjunction with KGSWC 2021

November 19- 24, 2021 – Online

https://kgswc.org/iwdlq2021/

Important dates:
• Workshop paper submission due: October 18, 2021
• Workshop paper notifications: October 29, 2021
• Workshop paper camera-ready versions due: November 08, 2021
• Workshop: November 19-24, 2021 (half-day)

 

All deadlines are 23:59 anywhere on earth (UTC-12).

Context of the workshop:

Question Answering System (QAS) is an important area in Artificial Intelligence.
Generating automatic response is a fastidious and time-consuming task for there exists only some very general approaches to understand the intent of users. Question Answering (QA) is applied in many domain applications such as medical, finance, e-commerce, etc. Given a list of documents, a QAS can provide the right answer to the query pose in natural language. It combines natural language processing (NLP), information retrieval (IR) and knowledge representation and reasoning (KRR) as a relevant component for this process. The general process of QA is composed of different steps: (i) user query, (ii) question analysis (simple or complex query, open or closed domain, linguistic layer, semantic layer, etc), (iii) answer retrieval, and (iv) answer extraction from a set of candidate ones. All these steps are important to answer correctly, precisely and briefly to the user native language question. The answer can refer to a term, a sentence, an image, an audio, a video or to the full textual document.

Recent Deep Learning approaches and information retrieval are implemented in order to reason over the questions and its links with the corresponding response. Modern NLP techniques make it possible for computers to read and interpret text, hear and understand speech, measure sentiment, and determine which parts in a document are important. Another important aspect of QAS is the integration of knowledge graphs (KGs) as a new dimension to provide a concise answer issued from the KG. KG is graph-based data model that structure and store real-world entities (abstract concepts) and their relationships (hierarchical and associative) in a graph. The KG is the most suitable and beneficial way to solve many challenging problems related to information domain.

Objective:

The first edition of this workshop aims at highlighting recent and future advances on
question answering systems over structured semantic and unstructured textual data and to demonstrate the role of deep learning algorithms to enrich this process. In addition to that, the goal of this workshop is to bring together an area for experts from industry, science and academia to exchange ideas and discuss results of on-going research in Question Answering approaches.

Topics of interests:
• Question answering over Linked Data
• Knowledge Graphs for Question Answering
• Complex Question Answering over texts and linked data
• Reasoning for Complex Question Answering
• Natural Language Processing based question answering
• Hybrid text and knowledge graph reasoning
• SPARQL query pattern generation
• Natural language querying of RDF exposed as Linked Data
• Ontology-based query answering
• Visual Question Answering
• Image question answering
• Audio and Speech Question Answering
• Video Question Answering
• Datasets combining structured and unstructured knowledge
• Applications of question answering
• and so on.

 

Submission:

The workshop is open to submit unpublished work resulting from research that presents original scientific results, methodological aspects, concepts and approaches. All submissions are not anonymous and must be PDF documents written in English and formatted using the following style files: KGSWC2021_authors_kit

Papers are to be submitted through the workshop's EasyChair submission page.

We welcome the following types of contributions:

  • Full papers (9-12 pages): Finished or consolidated R&D works, to be included in one of the Workshop topics.
  • Short papers (6-8 pages): Ongoing works with relevant preliminary results, opened to discussion.

 

Accepted papers are planned to publish with Springer Proceeding (Approval Pending).

At least one author of each accepted paper must register for the workshop, in order to present the paper. For further instructions, please refer to the KGSWC 2021 page.

 

Workshop chairs:
Sarra Ben Abbès, Engie, France

Rim Hantach, Engie, France

Philippe Calvez, Engie, France

 

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50 JAIIO – Programa preliminar de las 50 JAIIO

October 6th, 2021 Daniela Lopez de Luise

 

Estimados/as,

¿Querés ver el horario de tu exposición? ¿las conferencias que hay pensadas para vos? ¿las distintas charlas que se van a desarrollar?
 
¡Ya está listo el programa preliminar de las 50 JAIIO! 
 
En los próximos días recibirán información acerca de las plataformas y canales a través de los cuales se transmitirán las Jornadas.
 
Agendá las actividades que más te interesan, te esperamos del 18 al 29 de Octubre! 
 
¡No te las pierdas!
 
Saludos cordiales.
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DeepLearn 2022 Winter: early registration October 9

October 6th, 2021 Daniela Lopez de Luise
5th INTERNATIONAL SCHOOL ON DEEP LEARNING
DeepLearn 2022 Winter
Bournemouth, UK
January 17-21, 2022
***********
Co-organized by:
Department of Computing and Informatics
Bournemouth University
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
******************************************************************
Early registration: October 9, 2021
******************************************************************
SCOPE:
DeepLearn 2022 Winter will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw and Las Palmas de Gran Canaria.
Deep learning is a branch of artificial intelligence covering a spectrum of current exciting research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of different environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience.
Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main components of the event.
An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles.
ADDRESSED TO:
Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Winter is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators.

VENUE:
DeepLearn 2022 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be:
TBA

STRUCTURE:
3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
Full in vivo online participation will be possible. However, the organizers want to emphasize the importance of face to face interaction and networking in this kind of research training event.
KEYNOTE SPEAKERS:
Yi Ma (University of California, Berkeley), White-box Deep (Convolution) Networks from the Principle of Rate Reduction
Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks
Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills
PROFESSORS AND COURSES:
  • Peter L. Bartlett (University of California, Berkeley), [intermediate/advanced] Deep Learning: A Statistical Viewpoint
  • Joachim M. Buhmann (Swiss Federal Institute of Technology, Zürich), [introductory/advanced] Model and Algorithm Validation for Data Science
  • Matias Carrasco Kind (University of Illinois, Urbana-Champaign), [intermediate] Anomaly Detection
  • Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning
  • Seungjin Choi (BARO AI Academy), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces
  • Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare
  • Rüdiger Dillmann (Karlsruhe Institute of Technology), [introductory/intermediate] Building Brains for Robots
  • Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning
  • Charles Elkan (University of California, San Diego), [intermediate] AI and ML Applications in Finance and Retail
  • Rob Fergus (New York University), [intermediate/advanced] Self-supervised Learning of Visual Representations for Recognition and Interaction
  • João Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities
  • Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology
  • Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances
  • Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models
  • Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond
  • David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning
  • Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How?
  • Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning
  • Jude W. Shavlik (University of Wisconsin, Madison), [introductory/intermediate] Advising, Explaining, Distilling, and Quantizing Deep Neural Networks
  • Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning
  • Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks
  • Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning
  • Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis

OPEN SESSION:
An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david@irdta.eu by January 9, 2022.

INDUSTRIAL SESSION:
A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david@irdta.eu by January 9, 2022.
EMPLOYER SESSION:
Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david@irdta.eu by January 9, 2022.
ORGANIZING COMMITTEE:
Rashid Bakirov (Bournemouth, co-chair)
Nan Jiang (Bournemouth, co-chair)
Carlos Martín-Vide (Tarragona, program chair)
Sara Morales (Brussels)
David Silva (London, co-chair)
REGISTRATION:
It has to be done at
The selection of up to 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish.
Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will get exhausted. It is highly recommended to register prior to the event.
FEES:
Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline.
ACCOMMODATION:
Accommodation suggestions will be available in due time at
CERTIFICATE:
A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.
QUESTIONS AND FURTHER INFORMATION:
ACKNOWLEDGMENTS:
Bournemouth University
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
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Submit Your Research Articles – 2nd International Conference on Software Engineering, Security and Blockchain (SESBC 2021)

October 5th, 2021 Daniela Lopez de Luise

2 nd  International Conference on Software Engineering, Security andBlockchain (SESBC 2021)
December 24 ~ 25, 2021, Sydney, Australiahttps://cse2021.org/sesbc/index Scope2
nd
 International Conference on Software Engineering, Security and Blockchain (SESBC2021)
 Will provide an excellent international forum for sharing knowledge and results in theory,methodology and applications of Software Engineering, Security and Blockchain. Authors aresolicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in theareas of Software Engineering, Security and Blockchain.
pdf icon Submit_Your_Research_Articles_2nd_Intern.pdf
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2021 OkIP Intl Conf on Automated & Intelligent Systems|| OkCity, USA|| Nov 15-18

October 5th, 2021 Daniela Lopez de Luise

2021 OkIP International Conference on Automated and Intelligent Systems (CAIS)

MNTC Conference Center, Oklahoma City, OK, USA & Online

November 15-18, 2021

 

>> Co-located Conferences and Events

 

>> Keynotes/Invited Talks

“Machine Learning for Critical Systems Security”

– Nancy R. Mead, PhD, Carnegie Mellon University, USA

 

“Sustainable Energy Harvesting and Wireless Power Transfer Systems”

– Manos M. Tentzeris, PhD, Georgia Institute of Technology, USA

 

“Blockchain Technology and its implications in Business Applications and Healthcare IT”

– Akhil Kumar, PhD, Penn State University, USA

 

>> Technical Research & Industry Tracks

– Agent-based, Automated, and Distributed Supports

– Intelligent Systems and Applications

– Knowledge-based and Control Supports

– Robotics and Vehicles

 

>> Contribution Types

– Full Paper: Accomplished research results (6 pages)

– Short Paper: Work in progress/fresh developments (3 pages)

– Poster/Journal First: Displayed/Oral presented (1 page)

 

>> Important Dates (Extended):

– Submission: Oct 16, 2021

– Conference: Nov 15-18, 2021

 

>> Technical Program Committee

 

>> Venue

 

>> For more information, submission details, and important dates, visit:

 

Please feel free to contact us for any inquiry at:

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