Knowledge co-production for Indigenous adaptation pathways: transform post-colonial articulation complexes to empower local decision-making

Co-production between scientific and Indigenous knowledge has been identified as useful to generating adaptation pathways with Indigenous peoples, who are attached to their traditional lands and thus highly exposed to the impacts of climate change. However, ignoring the complex and contested histories of nation-state colonisation can result in naïve adaptation plans that increase vulnerability. Here, through a case study in central Australia, we investigate the conditions under which co-production between scientific and Indigenous knowledge can support climate change adaptation pathways among place-attached Indigenous communities. A research team including scientists, Ltyentye Apurte Rangers and other staff from the Central Land Council first undertook activities to co-produce climate change presentations in the local Arrernte language; enable community members to identify potential adaptation actions; and implement one action, erosion control. Second, we reflected on the outcomes of these activities in order to unpack deeper influences. Applying the theory of articulation complexes, we show how ideologies, institutions and economies have linked Indigenous societies and the establishing Australian nation-state since colonisation. The sequence of complexes characterised as frontier, mission, pastoral, land-rights, community-development and re-centralisation, which is current, have both enabled and constrained adaptation options. We found knowledge co-production generates adaptation pathways when: (1) effective methods for knowledge co-production are used, based on deeply respectful partnerships, cultural governance and working together through five co-production tasks—prepare, communicate, discuss, bring together and apply; (2) Indigenous people have ongoing connection to their traditional territories to maintain their Indigenous knowledge; (3) the relationship between the Indigenous people and the nation-state empowers local decision-making and learning, which requires and creates consent, trust, accountability, reciprocity, and resurgence of Indigenous culture, knowledge and practices. These conditions foster the emergence of articulation complexes that enable the necessary transformative change from the colonial legacies. Both these conditions and our approach are likely to be relevant for place-attached Indigenous peoples across the globe in generating climate adaptation pathways

Exploring Knowledge Transfer and Knowledge Building at Offshore Technical Support Centers

This is an exploratory investigation into knowledge transfer and knowledge building processes observed at offshore Technical Support Centers (TSCs) in China. Utilizing a multiple case study approach, the study examines how knowledge was transferred from the US-based support center to the China-based offshore support center, and how individuals and the organization built and expanded knowledge in a dynamic changing business context. The field cases were three Technical Support Centers in China. Three models were developed from the qualitative analysis of the field data to explain how knowledge is transferred and built in offshore TSCs. The knowledge transfer type adoption model identifies the relationships amongst the levels of knowledge (novice, advanced beginner, competency, and proficiency), the types of knowledge and the knowledge transfer approaches (structured transfer stages, unstructured copy, unstructured adaptation, and unstructured fusion). The basic individual tacit knowledge building model shows that tacit knowledge is acquired and built through two continuous knowledge building loops, an explicit learning loop and an implicit learning loop. The organizational knowledge building model demonstrates the interaction amongst knowledge flow, absorptive capacity, knowledge stock and knowledge intermediary in offshore knowledge transfer and building within the three levels (individual, group and organization levels) of the SECI spiral (socialization, externalization, combination and internalization). The three models provide new insights into the knowledge transfer process for different levels of knowledge acquisition, individual tacit knowledge building processes and organizational knowledge building processes in an offshore outsourcing business context. By applying these models to appropriate field situations, both practitioners and academics may be able to gain a deeper understanding of knowledge transfer approaches, be able to better guide new employees’ expertise and confidence building through controlled and monitored experiential learning process, and be able to improve understanding of how knowledge is built and evolves within organizations

SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL

One impressive emergent capability of large language models (LLMs) is generation of code, including Structured Query Language (SQL) for databases. For the task of converting natural language text to SQL queries, Text-to-SQL, adaptation of LLMs is of paramount importance, both in in-context learning and fine-tuning settings, depending on the amount of adaptation data used. In this paper, we propose an LLM-based Text-to-SQL model SQL-PaLM, leveraging on PaLM-2, that pushes the state-of-the-art in both settings. Few-shot SQL-PaLM is based on an execution-based self-consistency prompting approach designed for Text-to-SQL, and achieves 77.3% in test-suite accuracy on Spider, which to our best knowledge is the first to outperform previous state-of-the-art with fine-tuning by a significant margin, 4%. Furthermore, we demonstrate that the fine-tuned SQL-PALM outperforms it further by another 1%. Towards applying SQL-PaLM to real-world scenarios we further evaluate its robustness on other challenging variants of Spider and demonstrate the superior generalization capability of SQL-PaLM. In addition, via extensive case studies, we demonstrate the impressive intelligent capabilities and various success enablers of LLM-based Text-to-SQL.Comment: 16 page

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

Defining transformative climate science to address high-end climate change

Unidad de excelencia María de Maeztu MdM-2015-0552High-end climate change requires transformative solutions, as conventional strategies and solutions will not be enough if major disruptions in social-ecological systems are to be avoided. However, conventional climate assessment approaches and methods show many limitations if they are to provide robust knowledge and support to the implementation of such solutions in practice. To this end, we define transformative climate science as the open-ended process of producing, structuring, and applying solutions-oriented knowledge to fast-link integrated adaptation and mitigation strategies to sustainable development. In particular, based on our experiences within regional cases in Central Asia, Europe, Iberia, Scotland, and Hungary, we have selected 12 dimensions that scientists and practitioners can use as a checklist to design transformative-oriented climate assessments. While it is possible to talk both about transformative adaptation and transformative mitigation, in this paper, we make the case that societal transformation does not depend on mitigation or adaptation policies and actions, mostly because they are related to sustainability innovations, which are endogenous developments derived from deliberate social learning

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin

Coarse-to-Fine Adaptive People Detection for Video Sequences by Maximizing Mutual Information

Applying people detectors to unseen data is challenging since patterns distributions, such as viewpoints, motion, poses, backgrounds, occlusions and people sizes, may significantly differ from the ones of the training dataset. In this paper, we propose a coarse-to-fine framework to adapt frame by frame people detectors during runtime classification, without requiring any additional manually labeled ground truth apart from the offline training of the detection model. Such adaptation make use of multiple detectors mutual information, i.e., similarities and dissimilarities of detectors estimated and agreed by pair-wise correlating their outputs. Globally, the proposed adaptation discriminates between relevant instants in a video sequence, i.e., identifies the representative frames for an adaptation of the system. Locally, the proposed adaptation identifies the best configuration (i.e., detection threshold) of each detector under analysis, maximizing the mutual information to obtain the detection threshold of each detector. The proposed coarse-to-fine approach does not require training the detectors for each new scenario and uses standard people detector outputs, i.e., bounding boxes. The experimental results demonstrate that the proposed approach outperforms state-of-the-art detectors whose optimal threshold configurations are previously determined and fixed from offline training dataThis work has been partially supported by the Spanish government under the project TEC2014-53176-R (HAVideo

Continuous use of authoring for adaptive educational hypermedia : a long-term case study

Adaptive educational hypermedia allows lessons to be personalized according to the needs of the learner. However, to achieve this, content must be split into stand-alone fragments that can be processed by a course personalization engine. Authoring content for this process is still a difficult activity, and it is essential for the popularization of adaptive educational hypermedia that authoring is simplified, so that the various stakeholders in the educational process, students, teachers, administrators, etc. can easily work with such systems. Thus, real-world testing with these stakeholders is essential. In this paper we describe recent extensions and improvements we have implemented in the My Online Teacher MOT3.0 adaptation authoring tool set, based on an initial set of short-term evaluations, and then focus on describing a long-term usage and assessment of the system

How Dutch Institutions Enhance the Adaptive Capacity of Society

This report examines the adaptive capacity of the institutional framework of the Netherlands to cope with the impacts of climate change. Historically, institutions have evolved incrementally to deal with existing social problems. They provide norms and rules for collective action and create continuity rather than change. However, the nature of societal problems is changing as a result of the processes of globalization and development. With the progress made in the natural sciences, we are able to predict in advance, to a certain extent, the potential environmental impacts of various human actions on society, for example, climate change. This raises some key questions: Are our institutions capable of dealing with this new knowledge about future impacts and, more importantly, with the impacts themselves? Are our institutions capable of dealing with the inherent uncertainty of the predictions

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings