Recognizing Typeset Documents using Walsh Transformation

In this paper we present an effective character recognition algorithm, which can be applied mainly to typeset documents. Our aim was to compose a character recognition algorithm, which can be used to recognize simple typeset documents in a fast and reliable way. To get a good result by this algorithm the input text document should contain characters from the same character set with a small number of symbols. This condition does not mean a strong restriction as the documents in practice usually have this property. The main character recognition part of the algorithm is based on the Walsh transformation, which gives a verbose description about the image, like the symmetrical relations, placement of the foreground and background pixels, and so on. That is why we tried to apply it to recognize characters, and the algorithm proved to be fairly efficient and reliable for simple documents, since the feature vectors extracted by Walsh transformation can be well distinguished. Moreover, our method had very good results in tolerating different types of noise corruption

Testing the Consistency of Performance Scores Reported for Binary Classification Problems

Binary classification is a fundamental task in machine learning, with applications spanning various scientific domains. Whether scientists are conducting fundamental research or refining practical applications, they typically assess and rank classification techniques based on performance metrics such as accuracy, sensitivity, and specificity. However, reported performance scores may not always serve as a reliable basis for research ranking. This can be attributed to undisclosed or unconventional practices related to cross-validation, typographical errors, and other factors. In a given experimental setup, with a specific number of positive and negative test items, most performance scores can assume specific, interrelated values. In this paper, we introduce numerical techniques to assess the consistency of reported performance scores and the assumed experimental setup. Importantly, the proposed approach does not rely on statistical inference but uses numerical methods to identify inconsistencies with certainty. Through three different applications related to medicine, we demonstrate how the proposed techniques can effectively detect inconsistencies, thereby safeguarding the integrity of research fields. To benefit the scientific community, we have made the consistency tests available in an open-source Python package

Potential urban distribution of Phlebotomus mascittii Grassi and Phlebotomus neglectus Tonn. (Diptera: Psychodidae) in 2021–50 in Budapest, Hungary

Background & Objective: The most northern populations of two sand fly species (Phlebotomus mascittii and Phlebotomus neclectus) in the Carpathian Basin are known from Central Hungary. The most important limiting factor of the distribution of Phlebotomus species in the region is the annual minimum temperature which may be positively affected by the urban heat island and the climate change in the future. Method: Based on the latest case reports of the species, Climate Envelope Model was done for the period 1961-1990 and 2025-2050 to project the potential urban distribution of the species. The climatic data were obtained from RegCM regional climate model and MODIS satellite images. Results: The recent occurrence of the species in Central Hungary indicates that Phlebotomus species can overwinter in non-heated shelters in the built environment. Interpretation & Conclusion: Jointly heat island and future climate change seem to be able to provide suitable environment for the studied species in urban areas in a great extent