Reading and writing are the main interaction methods with web content. Text simplification tools are helpful for people with cognitive impairments, new language learners, and children as they might find difficulties in understanding the complex web content. Text simplification is the process of changing complex text into more readable and understandable text. The recent approaches to text simplification adopted the machine translation concept to learn simplification rules from a parallel corpus of complex and simple sentences. In this paper, we propose two models based on the transformer which is an encoder-decoder structure that achieves state-of-the-art (SOTA) results in machine translation. The training process for our model includes three steps: preprocessing the data using a subword tokenizer, training the model and optimizing it using the Adam optimizer, then using the model to decode the output. The first model uses the transformer only and the second model uses and integrates the Bidirectional Encoder Representations from Transformer (BERT) as encoder to enhance the training time and results. The performance of the proposed model using the transformer was evaluated using the Bilingual Evaluation Understudy score (BLEU) and recorded (53.78) on the WikiSmall dataset. On the other hand, the experiment on the second model which is integrated with BERT shows that the validation loss decreased very fast compared with the model without the BERT. However, the BLEU score was small (44.54), which could be due to the size of the dataset so the model was overfitting and unable to generalize well. Therefore, in the future, the second model could involve experimenting with a larger dataset such as the WikiLarge. In addition, more analysis has been done on the model’s results and the used dataset using different evaluation metrics to understand their performance.

Nonprofit Organizations (NPOs) are adopting technology to improve their quality of services, scale up, or reduce operation costs. However, due to the heterogeneity of systems they use, NPOs face system-integration challenges when collaborating with other organizations. Middleware is an intermediary software that assists dissimilar systems in working together. This paper explores middleware applications, opportunities, and challenges within the sector. It extensively reviewed the current state of research on middleware usage in the nonprofit sector for all papers published in Scopus and Web of Science (WoS) until 2023. Out of 127 papers returned, only 31 remained after removing duplicates, invalid entries, and out-of-scope publications. Then, we synthesized insights from a thorough survey of these selected papers. In light of the survey results, we observed that NPOs primarily use middleware in a few of the Sustainable Development Goals (SDGs), namely, health (SDG 3), NPO operations (SDG 8 and 9), NPO collaborations (SDG 17), development of sustainable cities (SDG 11), security and disaster management (SDG 16), and education (SDG 4). We also identified several challenges related to using middleware in the nonprofit sector, which include privacy, security, system development and performance, data processing and transfer, and volunteer attrition.