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4. Information Architecture as Theory: Sociotechnical Dimensions of Learning and Learning about Learning

   Piety, P. ; Wadhwani, N. ( June 2022 , International Conference of the Learning Sciences)

   This paper looks at technological advances in the collection and use of information about learning. Updating earlier discussions on transcripts and videorecording, the project used for examples in this paper features a digital simulation and pedagogical patterns where students met through videoconferencing as a post-pandemic alternative to table-base groupwork and then submitted transcripts of the meeting for evaluation and feedback. The transcripts were computationally analyzed to produce data streams showing the shape of conversations. These data were combined with records of students working on collaborative documents and the learning analytics for a digital simulation to illustrate new possibilities to depict collaborative student activity. Congruent with prior reflections on transcription of recordings and the recording process, this paper highlights the ways, old and new, the inscriptional process is not theory-neutral. It privileges certain activities and plays an agentive evidentiary role. 

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5. Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS

   https://doi.org/10.1109/COMST.2020.3036778  

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   Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This article is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this article, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area. 

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