Software Verification and Graph Similarity for Automated Evaluation of Students' Assignments

In this paper we promote introducing software verification and control flow graph similarity measurement in automated evaluation of students' programs. We present a new grading framework that merges results obtained by combination of these two approaches with results obtained by automated testing, leading to improved quality and precision of automated grading. These two approaches are also useful in providing a comprehensible feedback that can help students to improve the quality of their programs We also present our corresponding tools that are publicly available and open source. The tools are based on LLVM low-level intermediate code representation, so they could be applied to a number of programming languages. Experimental evaluation of the proposed grading framework is performed on a corpus of university students' programs written in programming language C. Results of the experiments show that automatically generated grades are highly correlated with manually determined grades suggesting that the presented tools can find real-world applications in studying and grading

A diagrammatic presentation of the category 3Cob

A category equivalent to the category of 3-dimensional cobordisms is defined in terms of planar diagrams. The operation of composition in this category is completely described via these diagrams.Comment: 26 page

Incorporating LLM Priors into Tabular Learners

We present a method to integrate Large Language Models (LLMs) and traditional tabular data classification techniques, addressing LLMs challenges like data serialization sensitivity and biases. We introduce two strategies utilizing LLMs for ranking categorical variables and generating priors on correlations between continuous variables and targets, enhancing performance in few-shot scenarios. We focus on Logistic Regression, introducing MonotonicLR that employs a non-linear monotonic function for mapping ordinals to cardinals while preserving LLM-determined orders. Validation against baseline models reveals the superior performance of our approach, especially in low-data scenarios, while remaining interpretable.Comment: Table Representation Learning Workshop at NeurIPS 202

Radon Exhalation Rates of Some Granites Used in Serbia

In order to address concern about radon exhalation in building material, radon exhalation rate was determined for different granites available on Serbian market. Radon exhalation rate, along with mass exhalation rate and effective radium content were determined by closed chamber method and active continuous radon measurement technique. For this research, special chambers were made and tested for back diffusion and leakage, and the radon concentrations measured were included in the calculation of radon exhalation. The radon exhalation rate ranged from 0.161 Bq/m(2)h to 0.576 Bq/m(2)h, the mass exhalation rate from 0.167 Bq/kgh to 0.678 Bq/kgh, while the effective radium content was found to be from 12.37 Bq/kg to 50.23 Bq/kg. The results indicate that the granites used in Serbia have a low level of radon exhalation

Reducing the Sodium Chloride Content in Chicken Pate by Using Potassium and Ammonium Chloride

AbstractThe aim of this research was to investigate possibility of chicken pate production with reduced sodium chloride content, as well as to establish changes in sensory characteristics. In the study, six experimental groups of chicken pate were produced with the same basic ingredients, but different amounts of added salts. Sensory evaluation was performed in order to determine general taste acceptability, and of the sodium and potassium levels in the chicken pate. The pate from EI and EII groups in which the amount of added sodium chloride was reduced and/or partially substituted with ammonium chloride had a most acceptable taste

Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models

Tabular data is often hidden in text, particularly in medical diagnostic reports. Traditional machine learning (ML) models designed to work with tabular data, cannot effectively process information in such form. On the other hand, large language models (LLMs) which excel at textual tasks, are probably not the best tool for modeling tabular data. Therefore, we propose a novel, simple, and effective methodology for extracting structured tabular data from textual medical reports, called TEMED-LLM. Drawing upon the reasoning capabilities of LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately inferring tabular features, even when their names are not explicitly mentioned in the text. This is achieved by combining domain-specific reasoning guidelines with a proposed data validation and reasoning correction feedback loop. By applying interpretable ML models such as decision trees and logistic regression over the extracted and validated data, we obtain end-to-end interpretable predictions. We demonstrate that our approach significantly outperforms state-of-the-art text classification models in medical diagnostics. Given its predictive performance, simplicity, and interpretability, TEMED-LLM underscores the potential of leveraging LLMs to improve the performance and trustworthiness of ML models in medical applications