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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.

The modern digitalization of cities occurs according to laws that are not yet clearly defined. The difficulty in understanding these laws is due to the high uncertainty of urban events and the difficulties in developing adequate analytical models for them. To substantiate management decisions on city digitalization in these conditions, effective methods for processing multidimensional time series are necessary. The objective is to develop a promising method. The proposed method is based on a new model of a neural network control system and algorithms for implementing its main functions. Distinctive features of the model include both structural aspects and new rules for neural network-based substantiation of management decisions on city digitalization, using unsupervised training of the applied recurrent neural network. This model provides for adaptive neural network management of city digitalization according to a sliding program. New rules are proposed for the forward and backward transformation of multidimensional time series, modeling the operation of a streaming recurrent neural network, and identifying feasible programs for city digitalization. By adhering to the proposed rules, it is possible not only to eliminate information loss in such a network but also to ensure its stable operation. The features of this neural network’s operation in substantiating feasible urban digitalization programs are described. The requirements for the software implementation of the proposed method are substantiated. The results of experiments using data for St. Petersburg are presented. These data included monthly volumes of shipped computers, electronic and optical products, as well as monthly volumes of shipped products for all manufacturing industries minus the first-time series values ??for the period from January 2021 to December 2025. The effectiveness of management decisions on the city’s digitalization was predicted based on the total volumes of shipped products to the horizon from January to December 2026. It is shown that the application of the proposed method allows for expanded capabilities for adaptively substantiating feasible management decisions on the city’s digitalization while reducing computer modeling costs.