Blockchain is a revolutionary technology that is constructively transforming many traditional industries, including financial services. Blockchain demonstrates immense potential in bringing substantial benefits to the remittance industry. Although the remittance industry has crossed the mark of USD 600 billion in 2021, remittance cost is still substantially high, around 6% on average, indirectly limiting financial inclusion and promoting de-risking. The involvement of multiple intermediaries in global remittances makes cross-border payments more expensive. Many projects, including Ripple and Stellar, employ blockchain technology to provide alternative infrastructure for cross-border payments. However, the decentralization of blockchain networks in both solutions is debatable. This paper examines the market characteristics impacting remittance cost, a prominent factor driving the evolution of the remittance industry. A truly decentralized blockchain framework viz. LayerOneX, which provides remittance services at a reduced cost, is proposed in this paper. Devices with low computation and memory capacity can act as transaction validators in this solution. A universal wallet across homogeneous and heterogeneous blockchains is proposed to facilitate fast and inexpensive remittance services. Thus, a novel framework for true decentralization of blockchain-based remittance services, resulting in reduced cost and, therefore, better financial inclusion, is proposed in this paper.

Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users’ query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user’s query with the minimum mismatches.

Over the last few years, the arena of mobile application development has expanded considerably beyond the balance of the world\'s software markets. With the growing number of mobile software companies, and the mounting sophistication of smartphones\' technology, developers have been building several categories of applications on dissimilar platforms. However, developers confront several challenges through the implementation of mobile application projects. In particular, there is a lack of consolidated systems that are competent to provide developers with personalised services promptly and efficiently. Hence, it is essential to develop tailored systems which can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a fortified set of techniques that are designed to provide mobile app developers with a distinctive platform to browse and search for the personalised artifacts. The proposed system make use of ontology and semantic web technology as well as machine learning techniques. In particular, the new RS framework comprises the following components; (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time-aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users\' query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user\'s query with the minimum mismatches.