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Text processing is a fundamental aspect of Natural Language Processing (NLP) and is crucial for various applications in fields such as artificial intelligence, data science, and information retrieval. It plays a core role in language models. Most text-processing approaches focus on describing and synthesizing, to a greater or lesser degree, lexical, syntactic, and semantic properties of text in the form of numerical vectors that induce a metric space, in which, it is possible to find underlying patterns and structures related to the original text. Since each approach has strengths and weaknesses, finding a single approach that perfectly extracts representative text properties for every task and application domain is hard. This paper proposes a novel approach capable of synthesizing information from heterogeneous state-of-the-art text processing approaches into a unified representation. Encouraging results demonstrate that using this representation in popular machine-learning tasks not only leads to superior performance but also offers notable advantages in memory efficiency and preservation of underlying information of the distinct sources involved in such a representation.

The rise of digitalization, sensory devices, cloud computing and internet of things (IoT) technologies enables the design of novel digital product lifecycle management (DPLM) applications for use cases such as manufacturing and delivery of digital products. The verification of the accomplishment/violations of agreements defined in digital contracts is a key task in digital business transactions. However, this verification represents a challenge when validating both the integrity of digital product content and the transactions performed during multiple stages of the DPLM. This paper presents a traceability method for DPLM based on the integration of online and offline verification mechanisms based on blockchain and fingerprinting, respectively. A blockchain lifecycle registration model is used for organizations to register the exchange of digital products in the cloud with partners and/or consumers throughout the DPLM stages as well as to verify the accomplishment of agreements at each DPLM stage. The fingerprinting scheme is used for offline verification of digital product integrity and to register the DPLM logs within digital products, which is useful in either dispute or violation of agreements scenarios. We built a DPLM service prototype based on this method, which was implemented as a cloud computing service. A case study based on the DPLM of audios was conducted to evaluate this prototype. The experimental evaluation revealed the ability of this method to be applied to DPLM in real scenarios in an efficient manner.

This paper introduces a file storage service that is implemented on a private/hybrid cloud computing environment. The entire system was implemented using open source software. The characteristic of elasticity is supported by virtualization technologies allowing to increase and to decrease the computing and storage resources based on their demand. An evaluation of performance and resource consumption was made using several levels of data availability and fault tolerance. The set of modules included in this storage environment can be taken as a reference guide for IT staff that wants to have some experience building a modest cloud storage infrastructure.