AnNoSense addresses the following foundational research concerns and suggests topics including (but are not limited to):
Understanding the impact of occurring novelties and anomalies in data streams on current established machine learning and recognition pipelines.
Establishing Frameworks and Guidelines that go beyond classical label based training and understanding towards an open world assumption where neither the infrastructure, the labels itself, nor the data is known in advance. These systems must be based on a deep autonomous identification of various cause- and effect relations to understand the underlying complex system.
Laying down new foundations for the modeling, detecting, and understanding of the inclusion of anomalies and novel appearing artifacts in the design and training of dynamic adaptable machine learning models.
Identifying Models and Tools on a theoretical and practical base, applicable for establishing autonomous and dynamic system behavior based on identified changes in the collected data streams.
Discuss new Software Engineering Perspectives on model integration in data driven workflows and application scenarios with respect to real-time behaviour, scalability, bootstrapping, dynamic learning, failure and error prevention and recovery, but also focusing on building trust of users in the emerging systems.
Dynamic \model training, adaption and replacement guided by reasoning processes dependent on detected novelties inducing an autonomous lifelong learning and adaptation process.
Semantic Modelling of heterogeneous data for Collaborative Reasoning that combines Machine Learning Models with fundamental Theory to identify latent Emerging Effects that are traceable to their origin, resulting in an explainable AI based system
Understanding and Dealing with the Impact of Systems that autonomously reacts on identified anomalies and novelties and alters its operating environment on an intervention based level.
Identify the need to make systems aware of the fact that they probably Change Environments and Behavior of People just based on their simple presence and, in addition by just the information they present to the user without even having the intention in the first place.
Reliability and Trustworthiness} have a significant impact on the user acceptance of dynamically changing systems. Addressing and presenting system changes to users in an Understandable and Explainable way is a major issue that needs to be addressed to successfully implement novelty driven systems. This additionally includes Ethics, Privacy, Accessibility, Fairness, and Interactivity in the design process, especially if vulnerable groups (e.g., patients, children, minorities, etc.) are affected.
Regular paper submissions must present original, highly innovative, prospective, and forward-looking research in one or more of the themes given above. Full papers must break new ground, present new insight, deliver a significant research contribution and provide validated support for its results and conclusions. The workshop solicits (i) conceptual papers describing proposals for novel methodologies, theories and principles that might be used to design, develop and build, analyse and operate anomaly and novelty driven self-organizing autonomous sensor data systems, (ii) observational, epistemological and user study papers to deliver evidence for possible future scenarios, and emerging platforms and technologies as well as (iii) system-development papers proposing ingenious, novel HW/SW platforms.
Click here for the Full Call for Papers (pdf)