A Knowledge-Based Model For Context-Aware Smart Service Systems

The advancement of the Internet of Things, big data, and mobile computing leads to the need for smart services that enable the context awareness and the adaptability to their changing contexts. Today, designing a smart service system is a complex task due to the lack of an adequate model support in awareness and pervasive environment. In this paper, we present the concept of a context-aware smart service system and propose a knowledge model for context-aware smart service systems. The proposed model organizes the domain and context-aware knowledge into knowledge components based on the three levels of services: Services, Service system, and Network of service systems. The knowledge model for context-aware smart service systems integrates all the information and knowledge related to smart services, knowledge components, and context awareness that can play a key role for any framework, infrastructure, or applications deploying smart services. In order to demonstrate the approach, two case studies about chatbot as context-aware smart services for customer support are presented

Оцінювання розміру PHP-застосунків з відкритим кодом за нелінійними регресійними моделями з різними факторами

Приходько, С. Б. Оцінювання розміру PHP-застосунків з відкритим кодом за нелінійними регресійними моделями з різними факторами = Estimating the size of open-source PHP-based apps by nonlinear regression models with various factors / С. Б. Приходько, М. В. Ворона // Зб. наук. пр. НУК. – Миколаїв : НУК, 2021. – № 1 (484). – С. 92–98.Анотація. Проблема оцінювання розміру програмного забезпечення (ПЗ) на ранній стадії програмного проекту є важливою, оскільки оцінка розміру програмного забезпечення використовується для прогнозування трудомісткості розробки ПЗ, включаючи PHP-застосунки з відкритим кодом. Метою роботи є підвищення точності оцінювання розміру PHP-застосунків з відкритим кодом. Об’єктом дослідження є процес оцінювання розміру PHP-застосунків з відкритим кодом. Предметом дослідження є трьох-факторні моделі нелінійної регресії з різними факторами для оцінювання розміру PHP-застосунків з відкритим кодом. Для побудови трьохфакторних моделей нелінійної регресії ми використовуємо метод, заснований на багатовимірних нормалізуючих перетвореннях та інтервалах прогнозування. Ці моделі побудовані на основі чотирьох-вимірного перетворенні Джонсона для сімейства SB негаусового набору даних із 44 застосунків, розміщених на GitHub. Набір даних був отриманий за допомогою інструмента PhpMetrics (https://phpmetrics.org/). Трьох-факторні моделі нелінійної регресії побудовані за метриками діаграми класів: кількість класів, середня кількість методів на клас, сума середнього аферентного та еферентного зв’язків на клас, середнє значення DIT (глибина дерева успадкування) на клас. Для порівняння точності прогнозування трьох-факторних нелінійних регресійних моделей ми використовували відомі показники точності прогнозування, такі як множинний коефіцієнт детермінації R2 , середня величина відносної похибки MMRE та відсоток прогнозування на рівні величини відносної помилки 0,25, PRED(0,25). Нелінійна регресійна модель, що побудована навколо кількості класів, середньої кількості методів на клас, середнього значення DIT на клас, має більше значення PRED(0,25) та приблизно однакові значення R2 та MMRE, що і модель, в якій третім фактором є сума середнього аферентного та еферентного зв’язків на клас. Наукова новизна отриманих результатів полягає в тому, що удосконалена трьох-факторна нелінійна регресійна модель для оцінювання розміру PHP-застосунків з відкритим кодом шляхом введення нового фактору – середнього значення DIT на клас. Це дозволило збільшити значення PRED(0,25) на 8%. Практична значимість отриманих результатів полягає у розробці ПЗ, що реалізує побудовану модель, sci-мовою для Scilab.Abstract. The problem of estimating the software size in the early stage of a software project is important because a software size estimate is used for predicting the software development efforts, including open-source PHP-based apps. The purpose of the work is to increase the prediction accuracy of early software size estimation of open-source PHPbased apps. The object of study is the process of estimating the software size of open-source PHP-based apps. The subject of study is the three-factor nonlinear regression models with various factors to estimate the software size of open-source PHP-based apps. To build the three-factor nonlinear regression models we use the technique based on the multivariate normalizing transformations and prediction intervals. These models are constructed based on the Johnson four-variate normalizing transformation for SB family of the non-Gaussian data set from 44 apps hosted on GitHub. The data set was obtained using the PhpMetrics tool (https://phpmetrics.org/). The three-factor nonlinear regression models are built around the metrics of class diagrams: the number of classes, the average number of methods per class, the sum of average afferent coupling and average efferent coupling per class, DIT (depth of inheritance tree) mean per class. To compare the prediction accuracy of the three-factor nonlinear regression models we used the well-known prediction accuracy metrics such as a multiple coefficient of determination R2 , a mean magnitude of relative error MMRE, and prediction percentage at the level of magnitude of relative error of 0.25, PRED(0.25). The nonlinear regression model constructed around the number of classes, the average number of methods per class, DIT mean per class has the larger PRED(0.25) value and about the same values of R2 and MMRE that the model in which the third factor is the sum of average afferent coupling and average efferent coupling per class. The scientific novelty of obtained results is that the three-factor nonlinear regression model for estimating the software size of open-source PHP-based apps has been improved by introducing a new factor – the DIT mean per class. This allowed us to increase the PRED(0.25) value by 8%. The practical importance of obtained results is that the software realizing the constructed model is developed in the sci-language for Scilab

A HEDGE ALGEBRAS BASED CLASSIFICATION REASONING METHOD WITH MULTI-GRANULARITY FUZZY PARTITIONING

During last years, lots of the fuzzy rule based classifier (FRBC) design methods have been proposed to improve the classification accuracy and the interpretability of the proposed classification models. Most of them are based on the fuzzy set theory approach in such a way that the fuzzy classification rules are generated from the grid partitions combined with the pre-designed fuzzy partitions using fuzzy sets. Some mechanisms are studied to automatically generate fuzzy partitions from data such as discretization, granular computing, etc. Even those, linguistic terms are intuitively assigned to fuzzy sets because there is no formalisms to link inherent semantics of linguistic terms to fuzzy sets. In view of that trend, genetic design methods of linguistic terms along with their (triangular and trapezoidal) fuzzy sets based semantics for FRBCs, using hedge algebras as the mathematical formalism, have been proposed. Those hedge algebras-based design methods utilize semantically quantifying mapping values of linguistic terms to generate their fuzzy sets based semantics so as to make use of fuzzy sets based-classification reasoning methods proposed in design methods based on fuzzy set theoretic approach for data classification. If there exists a classification reasoning method which bases merely on semantic parameters of hedge algebras, fuzzy sets-based semantics of the linguistic terms in fuzzy classification rule bases can be replaced by semantics - based hedge algebras. This paper presents a FRBC design method based on hedge algebras approach by introducing a hedge algebra- based classification reasoning method with multi-granularity fuzzy partitioning for data classification so that the semantic of linguistic terms in rule bases can be hedge algebras-based semantics. Experimental results over 17 real world datasets are compared to existing methods based on hedge algebras and the state-of-the-art fuzzy sets theoretic-based approaches, showing that the proposed FRBC in this paper is an effective classifier and produces good results

A Maximum Entropy Approach to Sentence Boundary Detection of Vietnamese Texts

International audienceWe present for the first time a sentence boundary detection system for identifying sentence boundaries in Vietnamese texts. The system is based on a maximum entropy model. The training procedure requires no hand-crafted rules, lexicon, or domain-specific information. Given a corpus annotated with sentence boundaries, the model learns to classify each occurrence of potential end-of-sentence punctuations as either a valid or invalid sentence boundary. Performance of the system on a Vietnamese corpus achieved a good recall ratio of about 95%. The approach has been implemented to create a software tool named vnSentDetector, a plug-in of the open source software framework vnToolkit which is intended to be a general framework integrating useful tools for processing of Vietnamese texts

Các độ đo thông tin tương hỗ đa biến có điều kiện

Mutual information of two variables is a measure of relationship between two variables: the larger this measure the stronger the dependence, and vice visa. However, mutual information does not indicate whether the relationship between the variables is direct or indirect. To detect "direct mutual relations", we can use conditional mutual information. In the previous studies, we have proposed the mutual information measures of multiple variables. There are many mutual information measures with more than two variables. Each of them is sensitive to a kind of relationships that may exist among the multiple variables. However, as mutual information of two variables, the multivariate mutual information measures do not show whether the multivariate relationships are direct or indirect. In this paper, we propose new multivariate conditional mutual information measures and show that they can detect indirect multivariate relationships through conditional variables.Thông tin tương hỗ (Mutual Information-MI) giữa hai biến đã được sử dụng để phát hiện mối quan hệ giữa hai biến; khi độ đo này lớn thì sự phụ thuộc giữa hai biến cũng lớn và ngược lại. Tuy nhiên, thông tin tương hỗ lại không cho ta biết mối quan hệ giữa các biến là trực tiếp hay gián tiếp. Để phát hiện quan hệ tương hỗ là trực tiếp hay gián tiếp, chúng ta có thể sử dụng thông tin tương hỗ có điều kiện đối với biến thứ ba (Conditional Mutual Information-CMI).   Trong các nghiên cứu trước đây, chúng tôi đã đề xuất các độ đo thông tin tương hỗ đa biến. Có rất nhiều độ đo thông tin tương hỗ khi số biến nhiều hơn hai, mỗi độ đo thể hiện một loại quan hệ có thể tồn tại giữa các biến. Tuy nhiên, cũng như thông tin tương hỗ của hai biến, các độ đo thông tin tương hỗ đa biến chỉ cho ta biết tồn tại hay không một mối quan hệ đa biến; nhưng không cho ta biết mối quan hệ tương hỗ đó là trực tiếp hay gián tiếp. Trong nghiên cứu này, chúng tôi đề xuất các độ đo thông tin tương hỗ đa biến có điều kiện và sử dụng chúng để phát hiện các mối quan hệ đa biến là trực tiếp hay gián tiếp thông qua biến điều kiện

Transfer AdaBoost SVM for Link Prediction in Newly Signed Social Networks using Explicit and PNR Features

AbstractIn signed social network, the user-generated content and interactions have overtaken the web. Questions of whom and what to trust has become increasingly important. We must have methods which predict the signs of links in the social network to solve this problem. We study signed social networks with positive links (friendship, fan, like, etc) and negative links (opposition, anti-fan, dislike, etc). Specifically, we focus how to effectively predict positive and negative links in newly signed social networks. With SVM model, the small amount of edge sign information in newly signed network is not adequate to train a good classifier. In this paper, we introduce an effective solution to this problem. We present a novel transfer learning framework is called Transfer AdaBoost with SVM (TAS) which extends boosting-based learning algorithms and incorporates properly designed RBFSVM (SVM with the RBF kernel) component classifiers. With our framework, we use explicit topological features and Positive Negative Ratio (PNR) features which are based on decision-making theory. Experimental results on three networks (Epinions, Slashdot and Wiki) demonstrate our method that can improve the prediction accuracy by 40% over baseline methods. Additionally, our method has faster performance time