Knowledge, Volume 2, Issue 1 (March 2022) – 10 articles

The world is suffering from a myriad of challenges. These are not only a direct result of the COVID-19 pandemic but also the evidence of a series of structural problems that existing socio-economic structures have. Inspired by real examples of social innovations and based on a selective literature review, this article debates six domains of opportunities for social innovation. These domains refer to well-being, finance and banking, work, technology, learning and education, and leadership and governance. The article ends with crucial implications for implementing change and a sustainable transition. Full article

Due to the non-played matches on the grounds of COVID-19 pandemics, the usual evaluation of the results of tournaments is biased. Matches won by default may cause unrealistic results. In this paper, an expedient method, the generalization of Thurstone method for more than two options, is applied. It is able to evaluate the results of the played matches without requiring equal matches’ numbers. This method takes the strength of the opposer into consideration as well. We apply the method for evaluating Handball Champions’ League’s results. We illustrate that it efficiently predicts the results in the future. Full article

The building-and-construction industry has been researched extensively over its life cycle regarding green and sustainable processes and techniques due to its major contributions towards energy consumption and its environmental impacts. Over the past decade, the construction stage of a building is often criticized for overlooking or approximating the environmental impacts as compared to other life-cycle stages of a building. This is evident through strong research findings regarding other building life-cycle stages in building-emission-assessment studies. With the drive towards digitization, the construction industry is receiving significant research attention in order to minimize environmental impacts at the construction stage. Despite these research initiatives, only a handful of recent review studies have systematically furnished current advances, gaps and future directions in environmentally sustainable building-construction techniques. The current study represents a systematic literature review of the environmental impacts at the building-construction stage with the objective of identifying the current findings, gaps and future research scopes. A bibliometric assessment revealed key author contributions, key research areas and collaboration aspects of research works related to environmental impacts of construction in building projects. Four major barriers and knowledge gaps in conducting a comprehensive assessment at the construction stage of a building were identified, including the lack of definition of a generic system boundary, difficulties in data collection, complex modeling issues and complications in the classification and analysis of emissions. The findings would provide key knowledge for passionate construction-industry stakeholders who are keen to benchmark green and sustainable construction practices in the building industry. Full article

In the realm of information, conversational search is a relatively new trend. In this study, we have developed, implemented, and evaluated a multiview conversational image search system to investigate user search behaviour. We have also explored the potential for reinforcement learning to learn from user search behaviour and support the user in the complex information seeking process. A conversational image search system may mimic a natural language discussion with a user via text or speech, and then assist the user in locating the required picture via a dialogue-based search. We modified and improved a dual-view search interface that displays discussions on one side and photos on the other. Based on the states, incentives, and dialogues in the initial run, we developed a reinforcement learning model and a customized search algorithm in the back end that predicts which reply and images would be provided to the user among a restricted set of fixed responses. Usability of the system was validated using methodologies such as Chatbot Usability Questionnaire, System Usability Scale, and User Experience Questionnaire, and the values were tabulated. The result of this usability experiment proved that most of the users found the system to be very usable and helpful for their image search. Full article

This article uses a qualitative research method to identify eighty elementary pre-service teachers’ conceptual representations concerning static electricity. We carry out this analysis using a paper and pencil questionnaire. This study shows that pre-service teachers have an erroneous understanding compared to those commonly accepted by the scientific community. The inaccurate representations identified are relevant for developing teaching strategies focused on conceptual conflict. Full article

This study shows a methodological approach to improve geospatial interpolation carried out with the Inverse Distance Weighted algorithm using distances and other parameters to which we attribute relative weights such as elevation. We also provide reliable information about better data output by elaborating a more realistic confidence interval with various percentages of reliability. We tested the methodology to monthly accumulated rainfall and temperatures recorded by multiple monitoring stations in the Puglia region in South Italy. The whole procedure has been called Augmented Inverse Distance Weighted and is tested with the ultimate goal of predicting missing values at a regional scale based on cross-validation techniques applied to a dataset consisting of ten years of precipitation data and five years of temperature data. The efficacy of this approach is evaluated using statistical scores regularly employed in the model’s evaluation studies. Results show that the improvements over the classical approach are remarkable and that the “augmented” method provides more accurate measurements of environmental variables. The main application of this algorithm is the possibility to provide the spatialisation of values of precipitation and temperature, or any other based on its own needs, at every point of the territory, playing a very important role in agricultural decision support systems and letting us identify frosts, drought events, climatic trends, accidental events, cyclicality and seasonality. Full article

Conversational systems are now applicable to almost every business domain. Evaluation is an important step in the creation of dialog systems so that they may be readily tested and prototyped. There is no universally agreed upon metric for evaluating all dialog systems. Human evaluation, which is not computerized, is now the most effective and complete evaluation approach. Data gathering and analysis are evaluation activities that need human intervention. In this work, we address the many types of dialog systems and the assessment methods that may be used with them. The benefits and drawbacks of each sort of evaluation approach are also explored, which could better help us understand the expectations associated with developing an automated evaluation system. The objective of this study is to investigate conversational agents, their design approaches and evaluation metrics. This approach can help us to better understand the overall process of dialog system development, and future possibilities to enhance user experience. Because human assessment is costly and time consuming, we emphasize the need of having a generally recognized and automated evaluation model for conversational systems, which may significantly minimize the amount of time required for analysis. Full article

Mobile devices have become the most used tool for a large number of tasks that we regularly perform such as relating them, searching for information, and in particular for making purchases. A situation that is frequently repeated in many areas is discovering an object that belongs to another person but we would be interested in being able to acquire it. However, the problem arises of knowing where to buy it. For example, this happens with the clothes that other people are wearing. Today, technology offers recognition mechanisms that can help solve this problem. This article presents an Android app that can recognize a book based on an image and offer places where it can be purchased. For this, Google technology was used to recognize objects from images and it has been combined with the information provided by Google Books to find stores that sell recognized books. In this way, a system has been created that makes it easier for any user to identify and purchase books that they discover at any given time. Full article

Inefficiencies naturally form as organizations grow in size and complexity. The knowledge required to address these inefficiencies is often stove-piped across different organizational silos, geographic locations, and professional disciplines. Crowdsourcing provides a way to tap into the knowledge and experiences of diverse groups of people to rapidly identify and more effectively solve inefficiencies. We developed a prototype crowdsourcing system based on design thinking practices to allow employees to build a shared mental model and work collaboratively to identify, characterize, and rank inefficiencies, as well as to develop possible solutions. We conducted a study to assess how presenting crowdsourced knowledge (votes/preferences, supporting argumentation, etc.) from employees affected organizational Decision Makers (DMs). In spite of predictions that crowdsourced knowledge would influence their decisions, presenting this knowledge to DMs had no significant effect on their voting for various solutions. We found significant differences in the mental models of employees and DMs. We offer various explanations for this behavior based on rhetorical analysis and other survey responses from DMs and contributors. We further discuss different theoretical explanations, including the effects of various biases and decision inertia, and potential issues with the types of knowledge elicited and presented to DMs. Full article

There is a high increase in approaches that receive as input a text and perform named entity recognition (or extraction) for linking the recognized entities of the given text to RDF Knowledge Bases (or datasets). In this way, it is feasible to retrieve more information for these entities, which can be of primary importance for several tasks, e.g., for facilitating manual annotation, hyperlink creation, content enrichment, for improving data veracity and others. However, current approaches link the extracted entities to one or few knowledge bases, therefore, it is not feasible to retrieve the URIs and facts of each recognized entity from multiple datasets and to discover the most relevant datasets for one or more extracted entities. For enabling this functionality, we introduce a research prototype, called LODsyndesis${}_{IE}$, which exploits three widely used Named Entity Recognition and Disambiguation tools (i.e., DBpedia Spotlight, WAT and Stanford CoreNLP) for recognizing the entities of a given text. Afterwards, it links these entities to the LODsyndesis knowledge base, which offers data enrichment and discovery services for millions of entities over hundreds of RDF datasets. We introduce all the steps of LODsyndesis${}_{IE}$, and we provide information on how to exploit its services through its online application and its REST API. Concerning the evaluation, we use three evaluation collections of texts: (i) for comparing the effectiveness of combining different Named Entity Recognition tools, (ii) for measuring the gain in terms of enrichment by linking the extracted entities to LODsyndesis instead of using a single or a few RDF datasets and (iii) for evaluating the efficiency of LODsyndesis${}_{IE}$. Full article