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1. Inducing Stealth Assessors from Game Interaction Data

   https://doi.org/10.1007/978-3-319-61425-0_18  

   Min, Wookhee; Frankosky, Megan; Mott, Bradford; Wiebe, Eric; Boyer, Kristy E; Lester, James (June 2017, Artificial Intelligence in Education)

   A key untapped feature of game-based learning environments is their capacity to generate a rich stream of fine-grained learning interaction data. The learning behaviors captured in these data provide a wealth of information on student learning, which stealth assessment can utilize to unobtrusively draw inferences about student knowledge to provide tailored problem-solving support. In this paper, we present a long short-term memory network (LSTM)-based stealth assessment framework that takes as input an observed sequence of raw game-based learning environment interaction data along with external pre-learning measures to infer students’ post-competencies. The framework is evaluated using data collected from 191 middle school students interacting with a game-based learning environment for middle grade computational thinking. Results indicate that LSTM-based stealth assessors induced from student game-based learning interaction data outperform comparable models that required labor-intensive hand-engineering of input features. The findings suggest that the LSTM-based approach holds significant promise for evidence modeling in stealth assessment. 

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2. Towards Debiasing DNN Models from Spurious Feature Influence

   https://doi.org/10.1609/aaai.v36i9.21185  

   Du, Mengnan; Tang, Ruixiang; Fu, Weijie; Hu, Xia (June 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   Recent studies indicate that deep neural networks (DNNs) are prone to show discrimination towards certain demographic groups. We observe that algorithmic discrimination can be explained by the high reliance of the models on fairness sensitive features. Motivated by this observation, we propose to achieve fairness by suppressing the DNN models from capturing the spurious correlation between those fairness sensitive features with the underlying task. Specifically, we firstly train a bias-only teacher model which is explicitly encouraged to maximally employ fairness sensitive features for prediction. The teacher model then counter-teaches a debiased student model so that the interpretation of the student model is orthogonal to the interpretation of the teacher model. The key idea is that since the teacher model relies explicitly on fairness sensitive features for prediction, the orthogonal interpretation loss enforces the student network to reduce its reliance on sensitive features and instead capture more task relevant features for prediction. Experimental analysis indicates that our framework substantially reduces the model's attention on fairness sensitive features. Experimental results on four datasets further validate that our framework has consistently improved the fairness with respect to three group fairness metrics, with a comparable or even better accuracy. 

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3. Modular Control of Boolean Network Models

   https://doi.org/10.1007/s11538-025-01471-9  

   Murrugarra, David; Veliz-Cuba, Alan; Dimitrova, Elena; Kadelka, Claus; Wheeler, Matthew; Laubenbacher, Reinhard (June 2025, Bulletin of Mathematical Biology)

   Abstract The concept of control is crucial for effectively understanding and applying biological network models. Key structural features relate to control functions through gene regulation, signaling, or metabolic mechanisms, and computational models need to encode these. Applications often focus on model-based control, such as in biomedicine or metabolic engineering. In a recent paper, the authors developed a theoretical framework of modularity in Boolean networks, which led to a canonical semidirect product decomposition of these systems. In this paper, we present an approach to model-based control that exploits this modular structure, as well as the canalizing features of the regulatory mechanisms. We show how to identify control strategies from the individual modules, and we present a criterion based on canalizing features of the regulatory rules to identify modules that do not contribute to network control and can be excluded. For even moderately sized networks, finding global control inputs is computationally challenging. Our modular approach leads to an efficient approach to solving this problem. We apply it to a published Boolean network model of blood cancer large granular lymphocyte (T-LGL) leukemia to identify a minimal control set that achieves a desired control objective. 

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4. Experience Report: Using the FABRIC Testbed to teach a Graduate Computer Networking course

   https://doi.org/10.1145/3641554.3701923  

   Wolosewicz, Alexander; Venkateshmurthy, Prajwal Somendyapanahalli; Sultana, Nik (February 2025, ACM)

   The curriculum for a graduate Computer Networking course in Computer Science typically includes activities that help students gain a variety of practical skills that complement the theoretical knowledge they learn during the course. These skills are developed through exercises that present students with scenarios in which they are to understand or cause specific communication behavior over a network. These exercises are constrained by the computer resources that students use for learning. Ideally those resources can be tuned to increase the fidelity of the network that a student is managing—and ultimately allow each student to fully control their own network. This paper describes the motivation, process, and challenges of delivering a graduate course in networking using resources on FABRIC—a publicly-funded, international testbed for research in networking. The paper analyzes the experience of teaching three graduate courses on networking, and reflects on using FABRIC to (1) ensure that students have equal access to a high-quality network environment (rather than rely on students’ individual laptops or self-managed school equipment), and (2) exploit the research testbed’s flexibility to develop a rich range of exercises for students. We discuss our lessons learned and share advice for other instructors. 

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5. Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data

   Coleman, Benjamin; Baraniuk, Richard G.; Shrivastava, Anshumali (July 2020, Proceedings of Machine Learning Research)

   null (Ed.)

   We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset. Our online sketching algorithm compresses an N element dataset to a sketch of size 𝑂(𝑁𝑏log3𝑁) in 𝑂(𝑁(𝑏+1)log3𝑁) time, where 𝑏<1. This sketch can correctly report the nearest neighbors of any query that satisfies a stability condition parameterized by 𝑏. We achieve sublinear memory performance on stable queries by combining recent advances in locality sensitive hash (LSH)-based estimators, online kernel density estimation, and compressed sensing. Our theoretical results shed new light on the memory-accuracy tradeoff for nearest neighbor search, and our sketch, which consists entirely of short integer arrays, has a variety of attractive features in practice. We evaluate the memory-recall tradeoff of our method on a friend recommendation task in the Google plus social media network. We obtain orders of magnitude better compression than the random projection based alternative while retaining the ability to report the nearest neighbors of practical queries. 

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