Semantic levels of domain-independent commonsense knowledgebase for visual indexing and retrieval applications

Building intelligent tools for searching, indexing and retrieval applications is needed to congregate the rapidly increasing amount of visual data. This raised the need for building and maintaining ontologies and knowledgebases to support textual semantic representation of visual contents, which is an important block in these applications. This paper proposes a commonsense knowledgebase that forms the link between the visual world and its semantic textual representation. This domain-independent knowledge is provided at different levels of semantics by a fully automated engine that analyses, fuses and integrates previous commonsense knowledgebases. This knowledgebase satisfies the levels of semantic by adding two new levels: temporal event scenarios and psycholinguistic understanding. Statistical properties and an experiment evaluation, show coherency and effectiveness of the proposed knowledgebase in providing the knowledge needed for wide-domain visual applications

Mining health knowledge graph for health risk prediction

Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients’ personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients’ health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the patterns of various observations, the research contributes to the work of practitioners by providing a multifaceted understanding of individual and public health

Consolidation of complex events via reinstatement in posterior cingulate cortex

It is well-established that active rehearsal increases the efficacy of memory consolidation. It is also known that complex events are interpreted with reference to prior knowledge. However, comparatively little attention has been given to the neural underpinnings of these effects. In healthy adult humans, we investigated the impact of effortful, active rehearsal on memory for events by showing people several short video clips and then asking them to recall these clips, either aloud (Experiment 1) or silently while in an MRI scanner (Experiment 2). In both experiments, actively rehearsed clips were remembered in far greater detail than unrehearsed clips when tested a week later. In Experiment 1, highly similar descriptions of events were produced across retrieval trials, suggesting a degree of semanticization of the memories had taken place. In Experiment 2, spatial patterns of BOLD signal in medial temporal and posterior midline regions were correlated when encoding and rehearsing the same video. Moreover, the strength of this correlation in the posterior cingulate predicted the amount of information subsequently recalled. This is likely to reflect a strengthening of the representation of the video's content. We argue that these representations combine both new episodic information and stored semantic knowledge (or "schemas"). We therefore suggest that posterior midline structures aid consolidation by reinstating and strengthening the associations between episodic details and more generic schematic information. This leads to the creation of coherent memory representations of lifelike, complex events that are resistant to forgetting, but somewhat inflexible and semantic-like in nature

Visual representation of concepts : exploring users’ and designers’ concepts of everyday products

To address the question on how to enhance the design of user-artefact interaction at the initial stages of the design process, this study focuses on exploring the differences between designers and users in regard to their concepts of an artefact usage. It also considers that human experience determines people’s knowledge and concepts of the artefacts they interact with, and broadens or limits their concept of context of use. In this exploratory study visual representation of concepts is used to elicit information from designers and users, and to explore how these concepts are influenced by their individual experience. Observation, concurrent verbal and retrospective protocols and thematic interviews are employed to access more in depth information about users’ and designers’ concepts. The experiment was conducted with designers and users who were asked about their concepts of an everyday product. Three types of data were produced in each session: sketches, transcriptions from retrospectives verbal reports and observations. Through an iterative process, references about context, use and experience were identified in the data collected; this led to the definition of a coding system of categories that was applied for the interpretation of visuals and texts. The methodology was tested through preliminary studies. Their initial outcomes indicate that the main differences between designers’ and users’ concepts come from their knowledge domain, while main similarities are related to human experience as source that drives concept formulation. Cultural background has been found to influence concepts about product usability and its context of use. The use of visual representation of concepts with retrospective reports and interviews allowed access to insightful information on how human experience influence people’s knowledge about product usability and its context of use. It is expected that this knowledge contributes to the enhancement of the design of product usability

Argumentation-based fault diagnosis for home networks

Home networks are a fast growing market but managing them is a difficult task, and diagnosing faults is even more challenging. Current fault management tools provide comprehensive information about the network and the devices but it is left to the user to interpret and reason about the data and experiment in order to find the cause of a problem. Home users may not have motivation or time to learn the required skills. Furthermore current tools adopt a closed approach which hardcodes a knowledge base, making them hard to update and extend. This paper proposes an open fault management framework for home networks, whose goal is to simplify network troubleshooting for non-expert users. The framework is based on assumption-based argumentation that is an AI technique for knowledge representation and reasoning. With the underlying argumentation theory, we can easily capture and model the diagnosis procedures of network administrators. The framework is rule-based and extensible, allowing new rules to be added into the knowledge base and diagnostic strategies to be updated on the fly.The framework can also utilise external knowledge and make distributed diagnosi

The effect of the use of indigenous knowledge-based physics comics of android-based marbles games on verbal representation and critical thinking abilities in physics teaching

This research aims to reveal the effectiveness of the use of an indigenous knowledge-based physics comic of Android-based marbles games on verbal representation and critical thinking abilities. It is a quasi-experiment applying the pretest-posttest control group design. The research sample consists of two classes: the control and experimental classes, each of which consists of 35 students established using the cluster random sampling technique. The effectiveness of the indigenous knowledge-based physics comic of marbles games was analyzed using the quantitative method applying the effect size analysis. The result of the effect size analysis obtained from Cohen’s f in verbal representation ability is 0.11 interpreted as medium effect size and critical thinking ability is 0.43 interpreted as large effect size. This shows that the developed indigenous knowledge-based physics comic of Android-based marbles games in physics teaching gives effects to verbal representation and critical thinking abilities of the students. In other words, the developed comic is effective in improving verbal representation and critical thinking abilitiesPeer Reviewe