Automated citation analysis is becoming increasingly important in assessing the scientific quality of publications and identifying patterns of collaboration among researchers. However, little attention has been paid to analyzing the scientific content of the citation context. This study presents an unsupervised citation detection method that uses semantic similarities between citations and candidate sentences to identify implicit citations, determine their functions, and analyze their sentiments. We propose different document vector models based on TF-IDF weights and word vectors and compare them empirically to calculate their semantic similarity. To validate this model for identifying implicit citations, we used deep neural networks and LDA topic modeling on two citation datasets. The experimental results show that the F1 values for the implicit citation classification are 88.60% and 86.60% when the articles are presented in abstract and full-text form, respectively. Based on the citation function, the results show that implicit citations provide background information and a technical basis, while explicit citations emphasize research motivation and comparative results. Based on the citation sentiment, the results showed that implicit citations tended to describe the content objectively and were generally neutral, while explicit citations tended to describe the content positively. This study highlights the importance of identifying implicit citations for research evaluation and illustrates the difficulties researchers face when analyzing the citation context.

When delivering digitalization of educational services, a specific administration plan should be established, designed for maximum access, quick recovery from failure, and lowering the length of time it is unavailable, all while guaranteeing good security performance. It should include responsibilities such as providing dedicated servers for digitizing educational services, increasing security by using encryption and security-compliant network protocols, and database administration. To speed up the digitization of educational services, it is recommended that all databases be placed on a single server that will serve as the publisher, and that separate databases be distributed to three different servers that will act as the subscribers.

The Internet of Things (IoT) is rapidly growing and affecting various aspects of our daily lives, allowing for data collection and sharing. The number of devices connected to the IoT environment is growing and implies instant surveillance of almost every action, sound, and move we encounter daily. This vast spread and usage of wearable devices raises the issue of high possibilities of several types of security concerns. The wide growth of such devices implies a substantial risk in data generation, storage, and transmission via IoT devices. Numerous studies have proposed privacy-preserving methods aiming to provide better privacy. However, the exponential growth of IoT data has resulted in a decline in the efficiency and performance of existing methods. This chapter proposes a generative privacy-preserving model for IoT data. The work in this research proposes a generative model to perturb data while preserving its features. The performance of the proposed model is measured using several criteria, such as classification accuracy, precision, recall, the area under the curve (AUC), and F-score. The evaluation metrics used show remarkable results in terms of accuracy and privacy. <!----> <!----> <!----> <!---->