-------- Forwarded Message --------
Subject: [AISWorld] ISCRAM2023 @ Omaha, NE, USA: Call for Submissions
Date: Tue, 1 Nov 2022 16:52:48 +0000
From: Deepak Khazanchi <khazanchi(a)unomaha.edu>
To: aisworld(a)lists.aisnet.org <aisworld(a)lists.aisnet.org>
Dear Colleagues:
Information Systems for Crisis Response and Management
(ISCRAM.org<http://iscram.org/>) is an international organization that
promotes research and the exchange of knowledge related to the design,
use, and evaluation of information systems and technology deployed for
emergency and disaster mitigation, preparation, response, and recovery.
It is a multidisciplinary domain and includes as its members medical
scientists, clinicians, social scientists, ethnographic researchers,
computer scientists, management scientists, and much more.
ISCRAM 2023 (http://iscram2023.net; https://twitter.com/ISCRAM2023;
https://www.facebook.com/iscram2023) is being held in Omaha, Nebraska
(USA) from May 28th to May 31st, 2023 with a pre-conference reception on
the 27th May. We are pleased to invite you to submit your contributions
in 18 different specialized tracks managed by 70+ track chairs from
around the world. Additionally we have some amazing keynote speakers
lined up. So join us an enjoy the Nebraska hospitality with a very
special evening events that are all included with the registration cost.
Further information on how to submit the call for new track proposals
and the proposal template can be found at ISCRAM 2023
webpage<https://www.unomaha.edu/college-of-information-science-and-technology/iscra…>.
Current accepted list of tracks are on the website.
Detailed calls for submissions are below:
Call for Papers -
https://www.unomaha.edu/college-of-information-science-and-technology/iscra…
Call for Panels/Workshops/Tutorials -
https://www.unomaha.edu/college-of-information-science-and-technology/iscra…
Call for Poster/Demonstrations -
https://www.unomaha.edu/college-of-information-science-and-technology/iscra…
Call for Doctoral Consortium -
https://www.unomaha.edu/college-of-information-science-and-technology/iscra…
Deepak
Deepak Khazanchi, Ph.D
Professor of Information Systems & Quantitative Analysis
Executive Director, Center for Management of Information Technology (CMIT)
Director, Executive Master of Science in IT
Fulbright Specialist (2014-16; Norway 2016; 2022-25)
Fulbright Core (2022; Austria)
College of Information Science & Technology
University of Nebraska at Omaha
Personal URL: http://dkhazanchi.com<http://dkhazanchi.com/>
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-------- Forwarded Message --------
Subject: [AISWorld] UMAP ’23: 31st ACM Conference on User Modeling,
Adaptation and Personalization: First Call for Papers
Date: Tue, 1 Nov 2022 08:33:58 +0200
From: George A. Papadopoulos <george(a)cs.ucy.ac.cy>
To: aisworld(a)lists.aisnet.org
*** First Call for Papers ***
UMAP ’23: 31st ACM Conference on User Modeling, Adaptation and
Personalization
June 26 - 29, 2023, St. Raphael Resort, Limassol, Cyprus
https://www.um.org/umap2023/
ACM UMAP is the premier international conference for researchers and
practitioners
working on systems that adapt to individual users or groups of users,
and that
collect, represent, and model user information. ACM UMAP is sponsored
by ACM SIGCHI and SIGWEB. User Modeling Inc., as the core Steering
Committee, oversees
the conference organization. The proceedings, published by ACM, will be
part of the
ACM Digital Library.
The theme of UMAP 2023 is "Personalization in Times of Crisis”.
Specifically, we welcome submissions that highlight the impact that
critical periods (such as the COVID-19 pandemic, ongoing wars, and
climate change, to name a few) can have on
user modeling, personalization, and adaptation of (intelligent) systems;
the focus is on investigations that capture how these trying times may
have influenced user behavior and whether new models are required.
While we encourage submissions related to this theme, the scope of the
conference
is not limited to the theme only. As always, contributions from
academia, industry, and other organizations discussing open challenges
or novel research approaches
are expected to be supported by rigorous evidence appropriate to the
claims (e.g., user study, system evaluation, computational analysis).
Important Dates
• Paper Abstracts: January 19, 2023 (mandatory)
• Full paper: January 26, 2023
• Notification: April 11, 2023
• Camera-ready: May 2, 2023
• Conference: June 26 - 29, 2023
Note: The submissions deadlines are at 11:59 pm AoE time (Anywhere on Earth)
Conference Topics
We welcome submissions related to user modeling, personalization, and
adaptation
of (intelligent) systems targeting a broad range of users and
domains. For detailed descriptions and the suggested topics for each
track please visit the UMAP 2023 website.
Personalized Recommender Systems
This track invites works from researchers and practitioners on recommender
systems. In addition to mature research works addressing technical
aspects of recommendations, we welcome research contributions that
address questions
related to user perception, decision-making, and the business value of
recommender systems.
Knowledge Graphs, Semantics, Social and Adaptive Web
This track welcomes works focused on the use of knowledge
representations (i.e.,
novel knowledge bases), graph algorithms (i.e., graph embedding
techniques), and social network analysis at the service of addressing
all aspects of personalization,
user model building, and personal experience in online social systems.
Moreover,
this track invites works in adaptive hypermedia, as well as semantic and
social web.
Intelligent User Interfaces
This track invites works exploring how to make the interaction between
computers
and people smarter and more productive, leveraging solutions from
human-computer
interaction, data mining, natural language processing, information
visualization, and
knowledge representation and reasoning.
Personalizing Learning Experiences through User Modeling
This track invites researchers, developers, and practitioners from
various disciplines
to submit their innovative learning solutions, share acquired
experiences, and discuss
their modeling challenges for personalized adaptive learning.
Responsibility, Compliance, and Ethics
Researchers, developers, and practitioners have a social responsibility
to account for
the impact that technologies have on individuals (users, providers, and
other stakeholders) and society. This track invites works related to the
science of building, maintaining, evaluating, and studying adaptive
systems that are fair, transparent, respectful of users’ privacy, and
beneficial to society.
Personalization for Persuasive and Behavior Change Systems
This track invites submissions focused on personalization and tailoring
for persuasive
technologies, including but not limited to personalization models, user
models, computational personalization, design, and evaluation methods.
It also welcomes work that brings attention to the user experience and
designing personalized and adaptive behavior change technologies.
Virtual Assistants, Conversational Interactions, and Personalized
Human-robot Interaction
This track invites works investigating new models and techniques for
adapting synthetic companions (e.g., virtual assistants, chatbots,
social robots) to individual users. With the conversational modality so
in vogue across disciplines, this track welcomes work highlighting the
model and deployment of synthetic companions driven by conversational
search and recommendation paradigms.
Research Methods and Reproducibility
This track invites submissions on methodologies to evaluate personalized
systems, benchmarks, and measurement scales, with particular attention
to the reproducibility of results and techniques. Furthermore, the track
looks for submissions that report new insights from reproducing existing
works.
Submission and Review Process
Submissions for any of the aforementioned tracks should have a maximum
length of *14 pages* (excluding references) in the ACM new single-column
format
(https://www.acm.org/publications/proceedings-template). (Papers of any
length up
to 14 pages are encouraged; reviewers will comment on whether the size
is appropriate for the contribution.) Additional review criteria and
submission link will be available shortly on the conference website:
https://www.um.org/umap2023/ .
Accepted papers will be included in the conference proceedings and
presented at the
conference. At least one author should register for the conference by
the early registration date cut-off.
UMAP uses a *double-blind* review process. Authors must omit their names
and affiliations from their submissions; they should also avoid obvious
identifying statements. For instance, citations to the authors' prior
work should be in the third person. Submissions not abiding by anonymity
requirements will be desk rejected.
UMAP has a *no dual submission* policy, which is why full paper
submissions should
not be currently under review at another publication venue. Further,
UMAP operates under the ACM Conference Code of Conduct
(https://www.acm.org/about-acm/policy-against-harassment).
Program Chairs
• Julia Neidhardt, TU Wien, Austria
• Sole Pera, TU Delft, The Netherlands
Track Chairs
Personalized Recommender Systems
• Noemi Mauro (University of Torino, Italy)
• Olfa Nasraoui (University of Louisville, USA)
• Marko Tkalcic (University of Primorska, Slovenia)
Knowledge Graphs, Semantics, Social and Adaptive Web
• Daniela Godoy (ISISTAN - CONICET/UNICEN University, Argentina)
• Cataldo Musto (University of Bari, Italy)
Intelligent User Interfaces
• Bart Knijnenburg (Clemson University, USA)
• Katrien Verbert (KU Leuven, Belgium)
• Wolfgang Wörndl (TU Munich, Germany)
Personalizing Learning Experiences through User Modeling
Oleksandra Poquet (TU Munich, Germany)
• Olga C. Santos (UNED, Spain)
Responsibility, Compliance, and Ethics
• Michael Ekstrand (Boise State University, USA)
• Peter Knees (TU Wien, Austria)
Personalization for Persuasive and Behavior Change Systems
• Federica Cena (University of Torino, Italy)
• Rita Orji (Dalhousie University, Canada)
• Jun Zhao (Oxford University, England)
Virtual Assistants, Conversational Interactions, and Personalized
Human-robot Interaction
• Li Chen (Hong Kong Baptist University, Hong Kong)
• Yi Zhang (University of California Santa Cruz, USA)
• Ingrid Zukerman (Monash University, Australia)
Research Methods and Reproducibility
• Dietmar Jannach (University of Klagenfurt, Austria)
• Alan Said (University of Gothenburg, Sweden)
Contact information: umap2023-program(a)um.org
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