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В работе рассматривается задача поиска в научной статье фрагментов с недостающими библиографическими ссылками с помощью автоматической бинарной классификации. Для обучения модели предложен метод контрастного семплирования, новшеством которого является рассмотрение контекста ссылки с учетом границ фрагмента, максимально влияющего на вероятность нахождения в нем библиографической ссылки. Обучающая выборка формировалась из автоматически размеченных семплов — фрагментов из трех предложений с метками классов «без ссылки» и «со ссылкой», удовлетворяющих требованию контрастности: семплы разных классов дистанцируются в исходном тексте. Пространство признаков строилось автоматически по статистике встречаемости термов и расширялось за счет конструирования дополнительных признаков — выделенных в тексте сущностей ФИО, чисел, цитат и аббревиатур.

Проведена серия экспериментов на архивах научных журналов «Правоприменение» (273 статьи) и «Журнал инфектологии» (684 статьи). Классификация осуществлялась моделями Nearest Neighbours, RBF SVM, Random Forest, Multilayer Perceptron, с подбором оптимальных гиперпараметров для каждого классификатора.

Эксперименты подтвердили выдвинутую гипотезу. Наиболее высокую точность показал нейросетевой классификатор (95%), уступающий по скорости линейному, точность которого при контрастном семплировании также оказалась высока (91–94 %). Полученные значения превосходят результаты, опубликованные для задач NER и анализа тональности на данных со сравнимыми характеристиками. Высокая вычислительная эффективность предложенного метода позволяет встраивать его в прикладные системы и обрабатывать документы в онлайн-режиме.

The paper studies the problem of searching for fragments with missing bibliographic links in a scientific article using automatic binary classification. To train the model, we propose a new contrast resampling technique, the innovation of which is the consideration of the context of the link, taking into account the boundaries of the fragment, which mostly affects the probability of presence of a bibliographic links in it. The training set was formed of automatically labeled samples that are fragments of three sentences with class labels «without link» and «with link» that satisfy the requirement of contrast: samples of different classes are distanced in the source text. The feature space was built automatically based on the term occurrence statistics and was expanded by constructing additional features — entities (names, numbers, quotes and abbreviations) recognized in the text.

A series of experiments was carried out on the archives of the scientific journals «Law enforcement review» (273 articles) and «Journal Infectology» (684 articles). The classification was carried out by the models Nearest Neighbors, RBF SVM, Random Forest, Multilayer Perceptron, with the selection of optimal hyperparameters for each classifier.

Experiments have confirmed the hypothesis put forward. The highest accuracy was reached by the neural network classifier (95%), which is however not as fast as the linear one that showed also high accuracy with contrast resampling (91–94%). These values are superior to those reported for NER and Sentiment Analysis on comparable data. The high computational efficiency of the proposed method makes it possible to integrate it into applied systems and to process documents online.

Monkeypox is a disease reemerging in 2022 which is caused by the monkeypox virus (MPV). This disease can be transmitted not only from rodents to humans, but also from humans to other humans, and even from the environment to humans. In this work, we propose a mathematical model to capture the dynamics of monkeypox transmission which involve three modes of transmission, namely, from rodents to rodents, rodents to humans, and from humans to other humans. In addition to the basic reproduction number, we investigate the stability of all equilibrium points analytically, including an implicit endemic equilibrium by applying the center manifold theorem. Moreover, the vaccination as an alternative solution to eradicate the monkeypox transmission is discussed and solved as an optimal control problem. The results of this study show that the transmission of monkeypox is directly affected by the internal infection rates of each population, i. e., the infection rate of the susceptible human by an infected human and the infection rate of the susceptible rodent by an infected rodent. Furthermore, the external infection rates, i. e., the infection rate of the susceptible human by an infected rodent also affects the transmission of monkeypox although it does not affect the basic reproduction number directly.

Monkeypox is a disease reemerging in 2022 which is caused by the monkeypox virus (MPV). This disease can be transmitted not only from rodents to humans, but also from humans to other humans, and even from the environment to humans. In this work, we propose a mathematical model to capture the dynamics of monkeypox transmission which involve three modes of transmission, namely, from rodents to rodents, rodents to humans, and from humans to other humans. In addition to the basic reproduction number, we investigate the stability of all equilibrium points analytically, including an implicit endemic equilibrium by applying the center manifold theorem. Moreover, the vaccination as an alternative solution to eradicate the monkeypox transmission is discussed and solved as an optimal control problem. The results of this study show that the transmission of monkeypox is directly affected by the internal infection rates of each population, i. e., the infection rate of the susceptible human by an infected human and the infection rate of the susceptible rodent by an infected rodent. Furthermore, the external infection rates, i. e., the infection rate of the susceptible human by an infected rodent also affects the transmission of monkeypox although it does not affect the basic reproduction number directly.