Experiencing OptiqueVQS: A Multi-paradigm and Ontology-based Visual Query System for End Users

This is author's post-print version, published version available on http://link.springer.com/article/10.1007%2Fs10209-015-0404-5Data access in an enterprise setting is a determining factor for value creation processes, such as sense-making, decision-making, and intelligence analysis. Particularly, in an enterprise setting, intuitive data access tools that directly engage domain experts with data could substantially increase competitiveness and profitability. In this respect, the use of ontologies as a natural communication medium between end users and computers has emerged as a prominent approach. To this end, this article introduces a novel ontology-based visual query system, named OptiqueVQS, for end users. OptiqueVQS is built on a powerful and scalable data access platform and has a user-centric design supported by a widget-based flexible and extensible architecture allowing multiple coordinated representation and interaction paradigms to be employed. The results of a usability experiment performed with non-expert users suggest that OptiqueVQS provides a decent level of expressivity and high usability and hence is quite promising

When Traumatic Stressors are Not Past, But Now: Psychosocial Treatment to Develop Resilience with Children and Youth Enduring Concurrent, Complex Trauma

The article discusses a project called the Empowering Counseling Program (ECP) conducted in community schools using participatory action and consumer evaluation designs. It addressed the elements of treatment theories used by mental health providers such as values, assumptions and concepts. It cites findings that clients suffering from complex trauma in under-resourced communities, unavoidably traumatized concurrently with treatment do not benefit from treatment guidelines

Faculty Senate Chronicle March 15, 1984

Minutes for the regular meeting of The University of Akron Faculty Senate on March 15, 1984

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

Animal-Assisted Interventions in Supervision: A Collective Case Study

Animal-assisted interventions (AAIs) have been discussed in recent conceptual literature as having potential for positive implications when applied in supervision (Chandler, 2017; Jackson, 2020; Owenby, 2017; Stewart et al., 2015). However, there was limited empirical foundation or guidance for the integration of two distinct specializations (AAIs and supervision). The purpose of this qualitative collective case study (Stake, 2006) was to explore and understand the experiences of supervisors who have been implementing AAIs within the context of supervision. Specifically, this study addressed the following overarching research question and two sub-questions were addressed: Q1 Why are supervisors integrating AAIs into supervision? Q1a What are the experiences of supervisors who have been integrating AAIs into supervision? Q1b How are supervisors integrating AAIs into clinical supervision? Three doctoral-level counseling professionals with extensive training and experience in AAIs participated, representing three cases of animal-assisted interventions in supervision (AAI-S). Participants had been practicing AAI-S between 7 and 10 years. Two cases existed within university-based, graduate-level AAI training programs and one case existed in the context of a private-practice. Five sources of data were collected for each participant (demographic questionnaire, professional documents [e.g., informed consent, supervisory disclosure statement], multiple interviews per participant [average of six hours per participant], which included a virtual tour of the AAI-S environment and introductions to animal partners). Data were analyzed using thematic analysis within and across cases (Braun & Clarke, 2009, 2021). Cross-case analysis suggested themes related to need for supportive context for implementation of AAI-S, professionals’ personal experiences associated with AAIs, common guiding frameworks for understanding the process of AAI-S, welfare and competency concerns, and the compelling rationale for AAI-S. The final report presented the findings as a holistic account of AAI-S. Based on the findings of this study, implications recommendations for counselor educators, supervisors, and professionals were provided as well as directions for future research