Ontological View-driven Semantic Integration in Open Environments

In an open computing environment, such as the World Wide Web or an enterprise Intranet, various information systems are expected to work together to support information exchange, processing, and integration. However, information systems are usually built by different people, at different times, to fulfil different requirements and goals. Consequently, in the absence of an architectural framework for information integration geared toward semantic integration, there are widely varying viewpoints and assumptions regarding what is essentially the same subject. Therefore, communication among the components supporting various applications is not possible without at least some translation. This problem, however, is much more than a simple agreement on tags or mappings between roughly equivalent sets of tags in related standards. Industry-wide initiatives and academic studies have shown that complex representation issues can arise. To deal with these issues, a deep understanding and appropriate treatment of semantic integration is needed. Ontology is an important and widely accepted approach for semantic integration. However, usually there are no explicit ontologies with information systems. Rather, the associated semantics are implied within the supporting information model. It reflects a specific view of the conceptualization that is implicitly defining an ontological view. This research proposes to adopt ontological views to facilitate semantic integration for information systems in open environments. It proposes a theoretical foundation of ontological views, practical assumptions, and related solutions for research issues. The proposed solutions mainly focus on three aspects: the architecture of a semantic integration enabled environment, ontological view modeling and representation, and semantic equivalence relationship discovery. The solutions are applied to the collaborative intelligence project for the collaborative promotion / advertisement domain. Various quality aspects of the solutions are evaluated and future directions of the research are discussed

Sharing economy and its development in China

Mestrado em Desenvolvimento e Cooperação InternacionalEsta dissertação fornece insights sobre o desenvolvimento da economia cooperativa na China. Com uma breve introdução inicial do que é economia cooperativa e seu surgimento na China. Usei dados de painel anuais na indústria de economia cooperativa da China durante os últimos anos. Em geral, experimentou um crescimento bastante rápido nos últimos anos, mas também surgiram alguns problemas e precisam de tratamentos adequados. Além disso, diante do surto da COVID-19, novas mudanças ocorreram e estão remodelando a indústria e a economia da China. Ao todo, a economia cooperativa ainda apresenta um grande potencial e algumas propostas são feitas para resolver os problemas existentes para melhor alcançar o desenvolvimento sustentável da indústria e da China.This dissertation provides insights on the development of sharing economy in China. With firstly brief introduction of what sharing economy is and its emergence in China. I used annual panel data in China?s sharing economy industry during the past few years. Generally, it has experienced quite fast growth in the past few years, but some problems also arose and need to be appropriately treated. What?s more, facing the outbreak of COVID-19, new changes have also occurred and are reshaping the industry and China?s economy. In all, sharing economy still shows a great potential and some proposes are made to solve the existing problems to better achieve the sustainable development of the industry and China.info:eu-repo/semantics/publishedVersio

TENT: Connect Language Models with IoT Sensors for Zero-Shot Activity Recognition

Recent achievements in language models have showcased their extraordinary capabilities in bridging visual information with semantic language understanding. This leads us to a novel question: can language models connect textual semantics with IoT sensory signals to perform recognition tasks, e.g., Human Activity Recognition (HAR)? If so, an intelligent HAR system with human-like cognition can be built, capable of adapting to new environments and unseen categories. This paper explores its feasibility with an innovative approach, IoT-sEnsors-language alignmEnt pre-Training (TENT), which jointly aligns textual embeddings with IoT sensor signals, including camera video, LiDAR, and mmWave. Through the IoT-language contrastive learning, we derive a unified semantic feature space that aligns multi-modal features with language embeddings, so that the IoT data corresponds to specific words that describe the IoT data. To enhance the connection between textual categories and their IoT data, we propose supplementary descriptions and learnable prompts that bring more semantic information into the joint feature space. TENT can not only recognize actions that have been seen but also ``guess'' the unseen action by the closest textual words from the feature space. We demonstrate TENT achieves state-of-the-art performance on zero-shot HAR tasks using different modalities, improving the best vision-language models by over 12%.Comment: Preprint manuscript in submissio