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Acute respiratory infections are a major public health concern because they are the leading cause of illness and death in many countries. Therefore, there is great interest in developing models and methods capable of modeling the spread of these infections within communities, with the aim of controlling outbreaks and preventing their spread. Agent-based models (ABM) are one of the most important tools in epidemiological research for modeling epidemic dynamics in realistic populations, but they face significant challenges in terms of computational complexity in their operation and calibration of epidemiological data, as parameter estimation typically requires repeated simulations across large parameter spaces to determine plausible values for key epidemiological parameters. This paper addresses the problem of alleviating computational constraints in the inverse problem of calibrating an ABM model for simulating the spread of respiratory infections in Saint Petersburg. The paper proposes the application of machine learning surrogate to link epidemic trajectories to underlying epidemiological parameters, enabling them to quickly infer parameter estimates from observed epidemic data. This is done by formulating the task of calibrating ABMs against epidemiological data as a supervised learning problem, where sequences extracted from epidemiological trajectories are associated with underlying epidemiological parameters. The research was based on evaluating the performance of attention-based sequence modeling, probabilistic deep learning, and distributional regression for inferring parameter estimates from truncated sequences of epidemic trajectories. Experimental evaluations have demonstrated the effectiveness of this approach and its practical and straightforward application. The results also indicated the superiority of attention-based sequence modeling, as it showed more consistent performance across metrics and horizons in accurate parameter estimation and credible uncertainty quantification. Distributional regression modeling also showed good performance with specific strengths in point accuracy while probabilistic deep learning performed poorly, especially at longer input horizons.

Disassembly of stripped x86 binaries is an important yet non-trivial task. Disassembly is difficult to perform correctly without debug information, especially on x86 architecture, which has variablesized instructions interleaved with data. Moreover, the presence of indirect jumps in binary code adds another layer of complexity. Indirect jumps impede the ability of recursive traversal, a common disassembly technique, to successfully identify all instructions within the code. Consequently, disassembling such code becomes even more intricate and demanding, further highlighting the challenges faced in this field. Many tools, including commercial ones such as IDA Pro, struggle with accurate x86 disassembly. As such, there has been some interest in developing a better solution using machine learning (ML) techniques. ML can potentially capture underlying compiler-independent patterns inherent for the compiler-generated assembly. Researchers in this area have shown that it is possible for ML approaches to outperform the classical tools. They also can be less timeconsuming to develop compared to manual heuristics, shifting most of the burden onto collecting a big representative dataset of executables with debug information. Following this line of work, we propose an improvement of an existing RGCN-based architecture, which builds control and flow graph on superset disassembly. The enhancement comes from augmenting the graph with data flow information. In particular, in the embedding we add Jump Control Flow and Register Dependency edges, inspired by Probabilistic Disassembly. We also create an open-source x86 instruction identification dataset, based on a combination of ByteWeight dataset and a selection open-source Debian packages. Compared to IDA Pro, a state of the art commercial tool, our approach yields better accuracy, while maintaining great performance on our benchmarks. It also fares well against existing machine learning approaches such as DeepDi.