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1. Predicting intent behind selections in scatterplot visualizations

   https://doi.org/10.1177/14738716211038604  

   Gadhave, Kiran; Görtler, Jochen; Cutler, Zach; Nobre, Carolina; Deussen, Oliver; Meyer, Miriah; Phillips, Jeff_M; Lex, Alexander (August 2021, Information Visualization)

   Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent. 

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2. A Unified Time Series Analytics based Intrusion Detection Framework for CAN BUS Attacks

   https://doi.org/10.1145/3626232.3653249  

   Maliha, Maisha; Bhattacharjee, Shameek (June 2024, ACM)

   Modern smart vehicles have a Controller Area Network (CAN) that supports intra-vehicle communication between intelligent Electronic Control Units (ECUs). The CAN is known to be vulnerable to various cyber attacks. In this paper, we propose a unified framework that can detect multiple types of cyber attacks (viz., Denial of Service, Fuzzy, Impersonation) affecting the CAN. Specifically, we construct a feature by observing the timing information of CAN packets exchanged over the CAN bus network over partitioned time windows to construct a low dimensional representation of the entire CAN network as a time series latent space. Then, we apply a two tier anomaly based intrusion detection model that keeps track of short term and long term memory of deviations in the initial time series latent space, to create a 'stateful latent space'. Then, we learn the boundaries of the benign stateful latent space that specify the attack detection criterion. To find hyper-parameters of our proposed model, we formulate a preference based multi-objective optimization problem that optimizes security objectives tailored for a network-wide time series anomaly based intrusion detector by balancing trade-offs between false alarm count, time to detection, and missed detection rate. We use real benign and attack datasets collected from a Kia Soul vehicle to validate our framework and show how our performance outperforms existing works. 

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3. Two entanglement conditions and their connection to negativity

   Hillery, Mark; Polvara, Camilla; Oganesyan, Vadim; Ali, Nada (February 2024, Physical Review A)

   We examine two conditions that can be used to detect bipartite entanglement, and show that they can be used to provide lower bounds on the negativity of states. We begin with two-qubit states, and then show how what was done there can be extended to more general states. The resulting bounds are then studied by means of a number of examples. We also show that if one has some knowledge of the Schmidt vectors of a state, better bounds can be found. 

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4. A Logic for Expressing Log-Precision Transformers

   Merrill, William; Sabharwal, Ashish (December 2023, NeurIPS 2023)

   One way to interpret the reasoning power of transformer-based language models is to describe the types of logical rules they can resolve over some input text. Recently, Chiang et al. (2023) showed that finite-precision transformers can be equivalently expressed in a generalization of first-order logic. However, finite-precision transformers are a weak transformer variant because, as we show, a single head can only attend to a constant number of tokens and, in particular, cannot represent uniform attention. Since attending broadly is a core capability for transformers, we ask whether a minimally more expressive model that can attend universally can also be characterized in logic. To this end, we analyze transformers whose forward pass is computed in logn precision on contexts of length n. We prove that any log-precision transformer can be equivalently expressed as a first-order logic sentence that, in addition to standard universal and existential quantifiers, may also contain majority-vote quantifiers. This is the tightest known upper bound and first logical characterization of log-precision transformers. 

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5. Using Demographic Pattern Analysis to Predict COVID-19 Fatalities on the US County Level

   https://doi.org/10.1145/3430196  

   Mueller, Klaus; Papenhausen, Eric (January 2021, Digital Government: Research and Practice)

   null (Ed.)

   Unlike pandemics in the past, COVID-19 has hit us in the midst of the information age. We have built vast capabilities to collect and store data of any kind that can be analyzed in myriad ways to help us mitigate the impact of this catastrophic disease. Specifically for COVID-19, data analysis can help local governments to plan the allocation of testing kits, testing stations, and primary care units, and it can help them in setting guidelines for residents, such as the need for social distancing, the use of face masks, and when to open local businesses that enable human contact. Further, it can also lead to a better understanding of pandemics in general and so inform policy makers on the regional and national level. All of this can save both cost and lives. In this article, we show the results of an ongoing study we conducted using a prominent regularly updated dataset. We used a pattern mining engine we developed to find specific characteristics of US counties that appear to expose them to higher COVID-19 mortality. Furthermore, we also show that these characteristics can be used to predict future COVID-19 mortality. 

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