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1. Decentralized Action Integrity for Trigger-Action IoT Platforms

   https://doi.org/10.14722/ndss.2018.23119  

   Fernandes, Earlence; Rahmati, Amir; Jung, Jaeyeon; Prakash, Atul (February 2018, Proceedings 2018 Network and Distributed System Security Symposium)

   Trigger-Action platforms are web-based systems that enable users to create automation rules by stitching together online services representing digital and physical resources using OAuth tokens. Unfortunately, these platforms introduce a longrange large-scale security risk: If they are compromised, an attacker can misuse the OAuth tokens belonging to a large number of users to arbitrarily manipulate their devices and data. We introduce Decentralized Action Integrity, a security principle that prevents an untrusted trigger-action platform from misusing compromised OAuth tokens in ways that are inconsistent with any given user’s set of trigger-action rules. We present the design and evaluation of Decentralized Trigger-Action Platform (DTAP), a trigger-action platform that implements this principle by overcoming practical challenges. DTAP splits currently monolithic platform designs into an untrusted cloud service, and a set of user clients (each user only trusts their client). Our design introduces the concept of Transfer Tokens (XTokens) to practically use finegrained rule-specific tokens without increasing the number of OAuth permission prompts compared to current platforms. Our evaluation indicates that DTAP poses negligible overhead: it adds less than 15ms of latency to rule execution time, and reduces throughput by 2.5%. 

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2. Rules and Rule-Making in the Five Largest Wikipedias

   Hwang, Sohyeon; Shaw, Aaron (August 2022, Proceedings of the International AAAI Conference on Weblogs and Social Media)

   The governance of many online communities relies on rules created by participants. However, prior work provides limited evidence about how these self-governance efforts compare and relate to one another across communities. Studies tend either to analyze communities as discrete entities or consider communities that coexist within a hierarchically-managed platform. In this paper, we investigate both comparative and relational dimensions of self-governance in similar communities. We use exhaustive trace data from the five largest language editions of Wikipedia over almost 20 years since their founding, and consider both patterns in rule-making and overlaps in rule sets. We find similar rule-making activity across the five communities that replicates and extends prior work on English language Wikipedia alone. However, we also find that these Wikipedias have increasingly unique rule sets, even as editing activity concentrates on rules shared between them. Self-governing communities aligned in key ways may share a common core of rules and rule-making practices as they develop and sustain institutional variations. 

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3. MELT: Mining Effective Lightweight Transformations from Pull Requests

   https://doi.org/10.1109/ASE56229.2023.00117  

   Ramos, Daniel; Mitchell, Hailie; Lynce, Inês; Manquinho, Vasco; Martins, Ruben; Goues, Claire Le (September 2023, 38th IEEE/ACM International Conference on Automated Software Engineering (ASE))

   Software developers often struggle to update APIs, leading to manual, time-consuming, and error-prone processes. We introduce Melt, a new approach that generates lightweight API migration rules directly from pull requests in popular library repositories. Our key insight is that pull requests merged into open-source libraries are a rich source of information sufficient to mine API migration rules. By leveraging code examples mined from the library source and automatically generated code examples based on the pull requests, we infer transformation rules in Comby, a language for structural code search and replace. Since inferred rules from single code examples may be too specific, we propose a generalization procedure to make the rules more applicable to client projects. Melt rules are syntax-driven, interpretable, and easily adaptable. Moreover, unlike previous work, our approach enables rule inference to seamlessly integrate into the library workflow, removing the need to wait for client code migrations. We evaluated Melt on pull requests from four popular libraries, successfully mining 461 migration rules from code examples in pull requests and 114 rules from auto-generated code examples. Our generalization procedure increases the number of matches for mined rules by 9×. We applied these rules to client projects and ran their tests, which led to an overall decrease in the number of warnings and fixing some test cases demonstrating MELT's effectiveness in real-world scenarios. 

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4. From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction

   Vacareanu, Robert; Valenzuela-Escárcega, Marco A.; Barbosa, George C.; Sharp, Rebecca; Surdeanu, Mihai (June 2022, LREC proceedings)

   While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand, can be more easily modified. However, crafting rules requires expertise in linguistics and the domain of interest, making it infeasible for most users. Here we attempt to combine the advantages of these two directions while mitigating their drawbacks. We adapt recent advances from the adjacent field of program synthesis to information extraction, synthesizing rules from provided examples. We use a transformer-based architecture to guide an enumerative search, and show that this reduces the number of steps that need to be explored before a rule is found. Further, we show that our synthesized rules achieve state-of-the-art performance on the 1-shot scenario of a task that focuses on few-shot learning for relation classification, and competitive performance in the 5-shot scenario. 

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5. Identifying Learning Rules From Neural Network Observables

   Nayebi, A; Srivastava, S; Ganguli, S; Yamins, D. (December 2020, Advances in neural information processing systems)

   null (Ed.)

   The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Many proposals have been suggested, including Hebbian mechanisms, explicit error backpropagation, and a variety of alternatives. It is an open question as to what specific experimental measurements would need to be made to determine whether any given learning rule is operative in a real biological system. In this work, we take a "virtual experimental" approach to this problem. Simulating idealized neuroscience experiments with artificial neural networks, we generate a large-scale dataset of learning trajectories of aggregate statistics measured in a variety of neural network architectures, loss functions, learning rule hyperparameters, and parameter initializations. We then take a discriminative approach, training linear and simple non-linear classifiers to identify learning rules from features based on these observables. We show that different classes of learning rules can be separated solely on the basis of aggregate statistics of the weights, activations, or instantaneous layer-wise activity changes, and that these results generalize to limited access to the trajectory and held-out architectures and learning curricula. We identify the statistics of each observable that are most relevant for rule identification, finding that statistics from network activities across training are more robust to unit undersampling and measurement noise than those obtained from the synaptic strengths. Our results suggest that activation patterns, available from electrophysiological recordings of post-synaptic activities on the order of several hundred units, frequently measured at wider intervals over the course of learning, may provide a good basis on which to identify learning rules. 

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