In most previous research, “semantic computing” refers to computational implementations of semantic reasoning. It lacks support from the formal theory of computation. To provide solid foundations for semantic computing, researchers propose a different understanding of semantic computing based on finite automata. This approach provides a computer theoretical approach to semantic computing. But finite automata are not capable enough to deal with imprecise knowledge. Therefore, in this paper, we provide foundations for semantic computing based on probabilistic automata. Even though traditional probabilistic automata can handle imprecise knowledge, their limitation resides in their being defined on a fixed finite input alphabet. This deeply restricts the abilities of automata. In this paper, we rebuild traditional probabilistic automata for semantic computing. Furthermore, our new probabilistic automata are robust enough to handle any alphabet as input. They have better performances in many applications. We provide an application for weather forecasting, a domain for which traditional probabilistic automata are not effective due to their finite input alphabet. Our new probabilistic automata can overcome these limitations.

The current study analyzed the emerging innovation ecosystem of China Pakistan Economic Corridor (CPEC) under One Belt One Road (OBOR). The purpose of this study was to analyze the inputs, processes, and outcomes of the CPEC innovation ecosystem. The mixed research method was applied to achieve the objective. A literature review of the CPEC and innovation ecosystem was used to build solid grounds. Semi-structured interviews were conducted with 89 stakeholders (business managers, industrialists, academicians, researchers, and government officials) from different areas of Pakistan. Advanced text analytic and content analysis software along with mathematical procedures were further used to extract the anchors (stocks or fixed assets) and processes (changes related to various anchors, or stocks). The innovation ecosystem of CPEC is based on six processes namely Chinese people's involvement, other countries' connectivity, project development, economic activities, trade enhancement, and industrial development. Upon the disruption of any process, this ecosystem would not sustain. The combination of these processes further generates a unique structure to lead CPEC as an innovation ecosystem. Therefore, such type of infrastructure development project is essential for the economic prosperity of this region which makes the CPEC a demand-driven project. So far, it is the first study conducted on CPEC as an innovation ecosystem by exploring the relationship between anchors and processes.

Modern world is a world of social media. X formerly known as Twitter is one of the leading social media platform that represents the public opinions from across the globe on every walk of life. These opinions are not just short texts but represent the sentiments of the public. The sentimental analysis of this large scale text data give insightful information to organizations, researchers, policy makers, and government institutes. Therefore, this work proposed a BERT (Bidirectional Encoder Representations from Transformers)-based sentiment analysis model. The model classifies the X (Twitter) text or tweets as positive, negative or neutral by analyzing the text data. The proposed model is evaluated on accuracy, precision, recall and F1 score. The experiment results showed that the proposed model yielded promising performance against all sentiment classes.