Abstract:

In this paper, we study the possibility of using Finite State Machine (FSM-) based methods for deriving finite test suites with guaranteed fault coverage for Input / Output automata. A method for deriving an FSM for a given automaton is proposed and it is shown that finite test suites derived for such an FSM are complete for two fault models based on Input/Output automata if they are applied within the framework of proper timeouts.

Abstract: The system for automated parallelization SAPFOR (System FOR Automated Parallelization) includes tools for program analysis and transformation. The main goal of the system is to reduce the complexity of program parallelization. SAPFOR system is focused on the investigation of multilingual applications in Fortran and C programming languages. The low-level LLVM IR representation is used in SAPFOR for program analysis. This representation allows us to perform various IR-level optimizations to improve the quality of program analysis. At the same time, it loses some features of the program, which are available in its higher level representation. One of these features is the multi-dimensional structure of the arrays. Data dependence analysis is one of the main problems which should be solved to automate program parallelization. Moreover, such an analysis belongs to the class of NP-hard problems. Knowledge of the multidimensional structure of arrays allows in many cases to take into account the structure of index expressions in calls to arrays and reduce the complexity of the analysis. In addition, the use of multi-dimensional arrays allows us to use multi-dimensional processor matrix and to parallelize a whole loop nests, rather than a single loop in the nest. So, parallelism of a program is going to be increased. These opportunities are natively supported in the DVM system. This paper discusses the approach used in the SAPFOR system to recover the form of multi-dimensional arrays by their linearized representation in LLVM IR. The proposed approach has been successfully evaluated on various applications including performance tests from the NAS Parallel Benchmarks suite.

Abstract: The article is devoted to dynamic adaptive testing of educational activity of high school students in Krasnoyarsk to solve mathematical problems of transformation of the graph of a quadratic function in electronic problem environments. Dynamic adaptive test simulator allowed to conduct: pre-test, diagnosing the level of residual knowledge of students; dynamic assessment of educational activities in the process of self-learning problem solving; post-test, diagnosing the level of training of students.

Abstract:

The paper extends and continues the work on a pilot system for detecting contradictions in court rulings. The tool aims to identify contradictions between court rulings on administrative offense cases and the legislative norms governing them. The previously presented pipeline, which includes rule-based document preprocessing, search over a structured repository of legal norms, and LLM fine-tuning using a LoRA adapter, demonstrated fairly high metrics [1].

However, this implementation had a number of limitations, primarily related to the RAG component. Unlike LLM, the RAG module was not trained on legal texts, which led to a substantial loss of relevant fragments for comparison at the retrieval stage.

The present work therefore focuses on a detailed quality analysis and improving the RAG component of the system. A dataset for evaluating premise retrieval recall was compiled, comprising 30 documents, along with automated tests containing all relevant sentence pairs (more than one hundred pairs in total). Using this data, the recall@n was calculated for the original (baseline) version of the model.

To improve the recall of the retrieved data, the following experiments were conducted: (1) fine-tuning the original embeddings on the system's training dataset; (2) adding a reranking model, both with and without fine-tuning; (3) combining variations of base/fine-tuned embeddings/reranker across several values of the top_k parameter (the number of retrieved matches); and (4) replacing the sentence embedding model with alternative options.

These experiments improved RAG recall from 72% to 90%. However, increasing the number of the retrieved fragments reduced the system's overall precision metrics, indicating a need for further training.

Other notable optimizations include reorganizing the structure of the legal codes database, expanding the test set, and improving the algorithm for filtering out irrelevant sentences from the rulings.