CfP: Special Issue on Deep Learning and Explainability for Sentiment Analysis – Electronics (IF: 2.412)
Webpage: https://www.mdpi.com/journal/electronics/special_issues/SA_electronics
Deadline: Dec 31, 2021
Call for Papers – Special Issue entitled “Human Activity Recognition Using Deep Learning”
June 23rd, 2021 
Daniela Lopez de Luise 
Registration for MIDL 2021 is now open!
June 23rd, 2021 Daniela Lopez de Luise We gave our best to keep the fees as low as possible with 125€ (Students: 90€). With your registration, you get access to the highly interactive and social (virtual) conference center in Gather.Town, to live presentations and panel discussions of each and every accepted submission, to poster sessions that mimic live sessions as close as possible, and to social and networking events throughout the days to match every time zone.
You can register at our website under https://2021.midl.io/
Also check the discounts for group registrations. This is the perfect chance to let your whole team participate in MIDL 2021.
We are looking forward to your registration!
The MIDL 2021 team
IEEE TLT Special Issue on Smart Learning Environments
June 23rd, 2021 Daniela Lopez de Luise IEEE Transactions on Learning Technologies
on
“TECHNOLOGIES FOR DATA-DRIVEN INTERVENTIONS
IN SMART LEARNING ENVIRONMENTS”
Guest Editors:
Pedro J. Muñoz-Merino – Universidad Carlos III de Madrid, Spain
Davinia Hernández-Leo – Universitat Pompeu Fabra, Spain
Miguel L. Bote-Lorenzo – Universidad de Valladolid, Spain
Dragan Gašević – Monash University, Australia
Sanna Järvelä – University of Oulu, Finland
Email: tlt-SLE@ieee.org
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( PDF version of this CFP available at https://bit.ly/3iApunv )
Smart learning environments (SLEs) have been recently defined [1] as learning ecologies wherein students perform learning tasks and/or teachers define them with the support provided by tools and technology. SLEs can encompass physical or virtual spaces in which a system senses the learning context and process by collecting data, analyzes the data, and consequently reacts with customized interventions that aim at improving learning [1]. In this way, SLEs may collect data about learners' and educators' actions and interactions related to their participation in learning activities as well as about different aspects of the formal or informal context in which they can be carried out, from sources such as learning management systems, handheld devices, computers, cameras, microphones, wearables, and environmental sensors. These data can then be transformed and analyzed using different computational and visualization techniques to obtain actionable information that can trigger a wid!
e range of automatic, human-mediated, or hybrid interventions that involve learners and teachers in the decision-making behind the interventions.
Data-driven interventions in SLEs can be mediated by technologies that are not only oriented to learners but also to teachers with the aim of helping learners succeed in their learning and educational goals. Examples of such technologies oriented to learners include dashboards supporting the self-regulation of their own learning processes [2], [3], systems adapting learning contents and learning activities [4], [5], feedback systems [6], and recommender systems [7], [8] of learning activities that are available in the context of the students and match their interests. Examples of technologies oriented to teacher interventions include tools and systems that support real-time classroom orchestration [9], redesign of learning paths [10], the improvement of contents, gamification, and formation of groups of students that are expected to engage in fruitful and productive collaboration [11].
Even though there is already a significant body of research around learning analytics and educational data mining techniques that can be applied to learning and teaching interventions [12], the specific theme of how to create and use technologies that facilitate the data-driven initiation, management, adaptation, and evaluation of such interventions in pedagogically sound and ethically defensible [13] ways within the context of SLEs still remains underexplored. This special issue aims to highlight and showcase new perspectives and advances in that regard, and to that end especially welcomes contributions reporting rigorous inquiries and investigations into the technical and design dimensions of technological solutions for enabling various types of intervention that can improve the efficiency and effectiveness of SLEs.
SUGGESTED TOPICS
Topics of interest for this special issue include, but are not limited to:
– Technology-supported data-driven interventions in different contexts (e.g., formal, informal) and spaces (e.g., physical, virtual) in SLEs;
– Systems based on learning analytics techniques (e.g., predictive models) to trigger interventions in SLEs;
– Tools based on visualization techniques (e.g., dashboards) and sensemaking approaches to trigger interventions in SLEs;
– Recommender systems supporting intervention processes in SLEs;
– Adaptive systems to optimize and personalize the learning and assessment process in SLEs;
– Multimodal and across-spaces systems based on learning analytics supporting technology-mediated interventions in SLEs;
– Internet of Things (IoT) technologies for data-driven interventions in SLEs;
– Technology-supported data-driven interventions based on analytics of data about teachers and their contexts in SLEs;
– Technological support of data-driven interventions in learning design and classroom orchestration in SLEs;
– Tools supporting data-driven interventions of students' regulation in SLEs;
– Linked Data and Semantic Web technologies to improve interventions in SLEs;
– Technologies in SLEs relying on specific strategies for data-driven interventions such as conversational agents, gamification mechanics, etc.;
– Ethical issues (e.g., fairness, accountability, transparency) in the design of technologies for facilitating data-driven interventions in SLEs.
Note: TLT is somewhat unique among educational technology journals in that it is both a computer science journal and an education journal. In order to be considered for publication in TLT, papers must make substantive technical and/or design-knowledge contributions to the development of learning technologies as well as show how the technologies can be used to support learning. Papers that are concerned primarily with evaluation of existing learning technologies and their applications are suitable for TLT only if the technologies themselves are novel, or if significant technical and/or design insights are offered.
KEY DATES
– Abstract submission (optional): 15 October 2021
– Feedback from guest editors to authors on abstracts: 31 October 2021
– Full manuscripts due: 15 December 2021
– Completion of first review round: 15 March 2022
– Revised manuscripts due: 30 April 2022
– Final decision notification: 30 June 2022
– Publication materials due: 15 September 2022
– Publication of special issue: Autumn/Fall 2022
SUBMISSION AND REVIEW PROCESS
Abstracts may be submitted to the guest editors via email at tlt-SLE@ieee.org ; this is not mandatory, but will enable the editors to offer early feedback on the paper's suitability with respect to the aims and scope of the special issue.
Full manuscripts should be prepared in accordance with the IEEE Transactions on Learning Technologies guidelines ( https://ieee-edusociety.org/publications/tlt-author-resources ) and submitted via the journal's ScholarOne Manuscripts portal ( https://mc.manuscriptcentral.com/tlt-cs ), being sure to select the relevant special issue name during the submission process. Manuscripts must not have been published or currently be under consideration for publication elsewhere. Only full manuscripts intended for review, not abstracts, should be submitted via the ScholarOne portal, and conversely, full manuscripts cannot be accepted via email.
Each full manuscript that passes an initial prescreening will be subjected to rigorous peer review in accordance with TLT's editorial policies and procedures. It is anticipated that between 7 and 11 articles (plus a guest editorial) will ultimately be published in the special issue.
(Sent to cvml@lists.auth.gr)
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CFP – KBS Special Issue on Deep Learning
June 23rd, 2021 Daniela Lopez de Luise Robust, Explainable, and Privacy-Preserving Deep Learning
Aim and Scope
The exponentially growing availability of data such as images, videos and speech from myriad sources, including social media and the Internet of Things, is driving the demand for high-performance data analysis algorithms. Deep learning is currently an extremely active research area in machine learning and pattern recognition. It provides computational models of multiple nonlinear processing neural network layers to learn and represent data with increasing levels of abstraction. Deep neural networks are able to implicitly capture intricate structures of large-scale data and deploy in cloud computing and high-performance computing platforms. The deep learning approach has demonstrated remarkable performances across a range of applications, including computer vision, image classification, face/speech recognition, natural language processing, and medical communications. However, deep neural networks yield ‘black-box’ input-output mappings that can be challenging to explain to users. Especially in the healthcare, cybersecurity, and legal fields, black-box machine learning techniques are unacceptable, since decisions may have a profound impact on peoples’ lives due to the lack of interpretability. In addition, many other open problems and challenges still exist, such as computational and time costs, repeatability of the results, convergence, and the ability to learn from a very small amount of data and to evolve dynamically. Further, despite their enormous societal benefits, deep learning can pose real threats to personal privacy. For example, deep neural networks and other machine learning models are built based on patients' personal and highly sensitive data such as clinical records or tracked health data in the domain of healthcare. Moreover, they can be vulnerable to attackers trying to infer the sensitive data that was used to build the model. This raises important research questions about how to develop deep learning models that protect private data against inference attacks while still being accurate and useful predictive models.
This Special Issue will present robust, explainable, and efficient next-generation deep learning algorithms with data privacy and theoretical guarantees for solving challenging artificial intelligence problems. This Special Issue aims to: 1) improve the understanding and explainability of deep neural networks; 2) improve the accuracy of deep learning leveraging new stochastic optimization and neural architecture search; 3) enhance the mathematical foundation of deep neural networks; 4) design new data privacy mechanisms to optimally tradeoff between utility and privacy; and 5) increase the computational efficiency and stability of the deep learning training process with new algorithms that will scale. Potential topics include but are not limited to the following:
· Novel theoretical insights on the deep neural networks
· Exploration of post-hoc interpretation methods which can shed light on how deep learning models produce a specific prediction and generate a representation
· Investigation of interpretable models which aim to construct self-explanatory models and incorporate interpretability directly into the structure of a deep learning model
· Quantifying or visualizing the interpretability of deep neural networks
· Stability improvement of deep neural network optimization
· Optimization methods for deep learning
· Privacy preserving machine learning (e.g., federated machine learning, learning over encrypted data)
· Novel deep learning approaches in the applications of image/signal processing, business intelligence, games, healthcare, bioinformatics, and security
Important Dates
· Submission Deadline: August 31, 2021
· First Review Decision: September 30, 2021
· Revisions Due: October 31, 2021
· Final Decision: November 30, 2021
· Final Manuscript: December 31, 2021
Dissemination, Composition and Review Procedures
· A Call for Papers (CFP) will be circulated to invite submissions.
· World leading researchers will be invited as authors.
· To further attracting contributors from around the world, the CFP will be advertised across numerous society newsletters, different websites, mailing lists, conferences, associations, and social media groups, etc.
This special issue will run as per the timeline given from submission to publication, while maintaining the rigorous peer review and high standards of the journal. All manuscripts submitted must be original, not under consideration elsewhere, and not previously published. A guide for authors and other relevant information for submission of manuscripts are available on the Guide for Authors’ page. Authors can expect their manuscripts to be reviewed fairly, and in a skilled, conscientious manner. To enhance objectivity, and to guarantee high scientific quality and relevance to the subject, three peer reviewers will be selected to evaluate a manuscript. The peer review process shall be designed to avoid bias and conflict of interest on the part of reviewers and shall be composed of experts in the relevant field of research. A key criterion in publication decisions will be the manuscript’s fit for the special issue and the readership of KBS. Papers will be published online as soon as accepted in continuous flow.
Submission Instructions
The submission system will be open around one week before the first paper comes in. When submitting your manuscript please select the article type “VSI: Deep Learning”. Please submit your manuscript before the submission deadline.
All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Please see an example here: https://www.sciencedirect.com/journal/science-of-the-total-environment/special-issue/10SWS2W7VVV
Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and the link to submit your manuscript is available on the Journal’s homepage.