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1. RELIC-FUN: Logic Identification through Functional Signal Comparisons

   https://doi.org/10.1109/DAC18072.2020.9218616  

   Geist, James ; Meade, Travis ; Zhang, Shaojie ; Jin, Yier ( July 2020 , 57th ACM/IEEE Design Automation Conference (DAC))

   null (Ed.)

   The ability to reverse engineer a hardware netlist in order to detect malicious logic has become an important problem in recent years. Much work has been done on algorithmically identifying structure and state in circuits; the first step of which is to separate control signals from data signals. The most current tools rely on topological comparisons of logic in order to identify signals which are uniquely structured in the netlist, as these signals are likely control signals. However, topological comparisons become less effective when a netlist has been resynthesized and optimized. We present a new tool, RELIC-FUN, based on netlist slicing and functional comparison of logic. Experimental results show that depending on netlist size, optimization, and control logic density, the proposed algorithm can be more accurate, and faster, than existing topological algorithms in many cases. 

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2. More than just fun: a place for games in playful learning / Más que diversión: el lugar de los juegos reglados en el aprendizaje lúdico

   https://doi.org/10.1080/02103702.2017.1292684  

   Hassinger-Das, Brenna ; Toub, Tamara S. ; Zosh, Jennifer M. ; Michnick, Jessica ; Golinkoff, Roberta ; Hirsh-Pasek, Kathy ( March 2017 , Infancia y Aprendizaje)
3. Fraud De-Anonymization for Fun and Profit

   https://doi.org/10.1145/3243734.3243770  

   Hernandez, Nestor ; Rahman, Mizanur ; Recabarren, Ruben ; Carbunar, Bogdan ( October 2018 , Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security)

   The persistence of search rank fraud in online, peer-opinion systems, made possible by crowdsourcing sites and specialized fraud workers, shows that the current approach of detecting and filtering fraud is inefficient. We introduce a fraud de-anonymization approach to disincentivize search rank fraud: attribute user accounts flagged by fraud detection algorithms in online peer-opinion systems, to the human workers in crowdsourcing sites, who control them. We model fraud de-anonymization as a maximum likelihood estimation problem, and introduce UODA, an unconstrained optimization solution. We develop a graph based deep learning approach to predict ownership of account pairs by the same fraudster and use it to build discriminative fraud de-anonymization (DDA) and pseudonymous fraudster discovery algorithms (PFD). To address the lack of ground truth fraud data and its pernicious impacts on online systems that employ fraud detection, we propose the first cheating-resistant fraud de-anonymization validation protocol, that transforms human fraud workers into ground truth, performance evaluation oracles. In a user study with 16 human fraud workers, UODA achieved a precision of 91%. On ground truth data that we collected starting from other 23 fraud workers, our co-ownership predictor significantly outperformed a state-of-the-art competitor, and enabled DDA and PFD to discover tens of new fraud workers, and attribute thousands of suspicious user accounts to existing and newly discovered fraudsters. 

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4. Exploring Learners Experience of Gamified Practicing: For Learning or for Fun?

   https://doi.org/10.17083/ijsg.v6i3.299  

   Dicheva, Darina ; Irwin, Keith ; Dichev, Christo ( September 2019 , International Journal of Serious Games)

   Gamification in education primarily aims at increasing learners’ motivation to engage in given learning activities and thereby promote desired learning behaviors. Despite its potential and the increasing number of gamified systems their success is still limited due to the fact that gamifying learning is a challenging process. Part of the challenge stems from an insufficient understanding of the motivational mechanisms of gamification on learners’ experience and from the insufficient attention to factors contributing to gameful experiences and promoting sustainable engagement.  In addition, there is limited empirical evidence on motivational influences driving students to participate in gamified learning activities. Another factor slowing down the progress of successfully gamified applications is the scarcity of tools that would enable instructors to design and implement gamified activities as part of their instructional strategy.  In an attempt to address these challenges, the present paper examines the difficulties inherent in the gamification of educational activities and how the OneUp gamification platform can support instructors in overcoming them with the mechanisms it provides for affording gameful experiences and sustainable engagement. To add to the understanding of the motivational influences towards gamified learning activities, the paper presents a preliminary study of student motivation to use OneUp for out-of-class practicing in a gamified Data Structures course. The outcomes of this study are based on data collected through a focus group, survey data, and system logs. 

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5. Double Your Corners, Double Your Fun: The Doorway Camera

   https://doi.org/10.1109/ICCP54855.2022.9887738  

   Krska, William ; Seidel, Sheila W. ; Saunders, Charles ; Czajkowski, Robinson ; Yu, Christopher ; Murray-Bruce, John ; Goyal, Vivek ( August 2022 , IEEE International Conference on Computational Imaging (ICCP))

   In a built environment, wanting to see without direct line of sight is often due to being outside a doorway. The two vertical edges of the doorway provide occlusions that can be exploited for non-line-of-sight imaging by forming corner cameras. While each corner camera can separately yield a robust 1D reconstruction, joint processing suggests novelties in both forward modeling and inversion. The resulting doorway camera provides accurate and robust 2D reconstructions of the hidden scene. This work provides a novel inversion algorithm to jointly estimate two views of change in the hidden scene, using the temporal difference between photographs acquired on the visible side of the doorway. Successful reconstruction is demonstrated in a variety of real and rendered scenarios, including different hidden scenes and lighting conditions. A Cramer-Rao bound analysis is used to demonstrate the 2D resolving power of the doorway camera over other passive acquisition strategies and to motivate the novel biangular reconstruction grid. 

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