1,720,958 research outputs found
Метод побудови та оптимізації маршруту для композитного веб-сервісу на основі Q-learning
Full text linkWe propose a method of automated flow generation for the web services composition according to the de fined target state based on reinforcement machine learning. An agent that uses Q-learning gradually accu mulates knowledge about the environment to updates the evaluations of the usefulness of its actions (these actions correspond to the existing services). The task is divided into two subtasks: - construction of possible flows represented as sequences of services where the results of the previous service execution change the current environment state and enable the exe cution of the next service; - choice of the optimal flow according to the history of interactions and to QoS criteria that is adapted to environment changes. We determine the main components of reinforcement learning and analyze their specifics for service com position task. Additional approaches that allow avoiding looping and the use of unnecessary services are considered. We propose modification of the Q-learning method developed for automatic generation of flows based on input and output data of web services and for selecting the optimal flow based on the analysis of their qualitative characteristics. This modified method uses approach with memory where the agent expands its knowledge about the environment at each step. We consider characteristics of proposed method based on analysis of its software implementation. Possibilities of proposed method are considered on example of generation an optimal study sequences used for individual educational trajectories in accordance with the personal needs of students. Every learning ob ject (information object used for educational needs described by metadata) is considered as a specific ser vice where inputs and outputs are represented by required and result competencies.Problems in programming 2025; 1: 82-93Запропоновано метод автоматизованої генерації маршруту для композиції вебсервісів відповідно до поставленої цілі з використанням методів машинного навчання з підкріпленням (reinforcement learning). Агент, використовуючи Q-learning, поступово накопичує знання про середовище та онов лює оцінку корисності своїх дій, яким відповідають наявні сервіси. Задача поділяється на дві підзадачі: побудову можливих маршрутів – таких послідовностей сервісів, що результати виконання попереднього сервісу змінюють середовище, уможливлюючи виконання наступного; та вибір оптимального маршруту відповідно до критеріїв QoS, що адаптується до змін самого середовища. Визначено основні складові навчання з підкріпленням, розглянуто їхню специфіку для аналізу серві сів. Розглянуто додаткові підходи, які дозволяють уникнути зациклення та використання непотріб них сервісів. Запропоновано модифікацію методу Q-learning для автоматичної генерації маршрутів на основі вхідних і вихідних даних вебсервісів і для вибору оптимального маршруту на основі аналі зу їхніх якісних характеристик з використанням підходу з пам’яттю, де агент із кожним кроком роз ширює свої знання про середовище. Запропоновано програмну реалізацію розробленого методу, яка дозволяє оцінити його властивості. Розглянуто можливості запропонованого методу на прикладі генерації оптимальних послідовностей вивчення матеріалів для індивідуальних освітніх траєкторій відповідно до особистих потреб студен тів. Кожен навчальний об’єкт (інформаційний об’єкт, що використовується для освітніх потреб та описаний метаданими) розглядається як окремий сервіс, де входи та виходи представлені необхідни ми та результуючими компетенціями.Problems in programming 2025; 1: 82-93
The technology of machine learning for a composite web service development
Full text linkWe analyze dynamic programming and machine learning algorithms (on example of Q-learning) used for automatic adaptive composition of web services based on service quality assessments, their input parameters and work specifics. Software implementation of these algorithms on sets of services of different volumes is developed for comparison their performance parameters. We determine that the considered methods allow finding the optimal set of services only for composition with a predefined fixed-length route. This restriction causes a need to generalize the problem formulation for an arbitrary set of service classes in the composition route. On base of the performed analysis, we developed an algorithm that solves this problem of building a composite service with a route of arbitrary length (using the Q-Learning method), that has the best overall quality ratings. A software implementation of both this algorithm and other algorithms for solving this problem (genetic algorithm, greedy search, dynamic programming, SARSA, etc.) are developed to compare the speed of their work and the evaluation of the resulting composite service on data sets of different volumes.Prombles in programming 2024; 4: 3-1
Flow constructing and optimizing method for composite web service based on Q-learning
Full text linkWe propose a method of automated flow generation for the web services composition according to the de fined target state based on reinforcement machine learning. An agent that uses Q-learning gradually accu mulates knowledge about the environment to updates the evaluations of the usefulness of its actions (these actions correspond to the existing services). The task is divided into two subtasks: - construction of possible flows represented as sequences of services where the results of the previous service execution change the current environment state and enable the exe cution of the next service; - choice of the optimal flow according to the history of interactions and to QoS criteria that is adapted to environment changes. We determine the main components of reinforcement learning and analyze their specifics for service com position task. Additional approaches that allow avoiding looping and the use of unnecessary services are considered. We propose modification of the Q-learning method developed for automatic generation of flows based on input and output data of web services and for selecting the optimal flow based on the analysis of their qualitative characteristics. This modified method uses approach with memory where the agent expands its knowledge about the environment at each step. We consider characteristics of proposed method based on analysis of its software implementation. Possibilities of proposed method are considered on example of generation an optimal study sequences used for individual educational trajectories in accordance with the personal needs of students. Every learning ob ject (information object used for educational needs described by metadata) is considered as a specific ser vice where inputs and outputs are represented by required and result competencies.Problems in programming 2025; 1: 82-9
Технологія використання машинного навчання для побудови композиційного веб-сервісу
Full text linkWe analyze dynamic programming and machine learning algorithms (on example of Q-learning) used for automatic adaptive composition of web services based on service quality assessments, their input parameters and work specifics. Software implementation of these algorithms on sets of services of different volumes is developed for comparison their performance parameters. We determine that the considered methods allow finding the optimal set of services only for composition with a predefined fixed-length route. This restriction causes a need to generalize the problem formulation for an arbitrary set of service classes in the composition route. On base of the performed analysis, we developed an algorithm that solves this problem of building a composite service with a route of arbitrary length (using the Q-Learning method), that has the best overall quality ratings. A software implementation of both this algorithm and other algorithms for solving this problem (genetic algorithm, greedy search, dynamic programming, SARSA, etc.) are developed to compare the speed of their work and the evaluation of the resulting composite service on data sets of different volumes.Prombles in programming 2024; 4: 3-13Проаналізовано алгоритми динамічного програмування та машинного навчання (на прикладі Q-learning), що використовуються для автоматичної адаптивної композиції веб-сервісів на основі оцінок якості сервісів, їхні вхідні параметри та особливості роботи. Для порівняння параметрів роботи цих алгоритмів на наборах сервісів різного обсягу розроблено програмну реалізацію. Визначено, що розглянуті методи дозволяють знаходити оптимальний набір сервісів тільки для композиції з попередньо визначеним маршрутом фіксованої довжини. Тому виникає потреба узагальнити постановку задачі для довільного набору класів у маршруті композиції сервісів. На основі виконаного аналізу розроблено алгоритм розв’язання цієї задачі – побудови композитного сервісу з маршрутом довільної довжини методом Q-Learning, що має найкращі сумарні оцінки якості. Розроблено програмну реалізацію як цього алгоритму, так і інших алгоритмів розв’язання цієї задачі (генетичний алгоритм, жадібний пошук, динамічне програмування, SARSA), яка дозволила порівняти швидкість їх роботи та оцінки результуючого композитного сервісу на наборах даних різного обсягу.Prombles in programming 2024; 4: 3-1
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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