Query Formulation Assistance for Kids: What is Available, When to Help & What Kids Want

Children use popular web search tools, which are generally designed for adult users. Because children have different developmental needs than adults, these tools may not always adequately support their search for information. Moreover, even though search tools offer support to help in query formulation, these too are aimed at adults and may hinder children rather than help them. This calls for the examination of existing technologies in this area, to better understand what remains to be done when it comes to facilitating query-formulation tasks for young users. In this paper, we investigate interaction elements of query formulation–including query suggestion algorithms–for children. The primary goals of our research efforts are to: (i) examine existing plug-ins and interfaces that explicitly aid children’s query formulation; (ii) investigate children’s interactions with suggestions offered by a general-purpose query suggestion strategy vs. a counterpart designed with children in mind; and (iii) identify, via participatory design sessions, their preferences when it comes to tools / strategies that can help children find information and guide them through the query formulation process. Our analysis shows that existing tools do not meet children’s needs and expectations; the outcomes of our work can guide researchers and developers as they implement query formulation strategies for children

IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning

The assets in Industry 4.0 are categorised into physical, virtual and human. The innovation and popularisation of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc. for assets identification and tracking. The generated data from Industrial Internet of Things (IIoT) eases information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of manufacturing equipment. Predictive maintenance enables the business owner to decide such as repairing or replacing the component before an actual failure which affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimise the tasks distributions and predictive maintenance model. This paper presents the Genetic Algorithm (GA) based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost and energy performance of GA along with MinMin, MaxMin, FCFS, RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time datasets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost and energy usage. The execution time is 0.48%faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively

Método de corrección ortográfica basado en trigramas y distancia de edición

En este trabajo se exponen los primeros resultados obtenidos de evaluación de un método de corrección ortográfica. Éste permite identificar errores y generar una lista de posibles reemplazos ordenada de acuerdo a la distancia que las sugerencias mantienen con la palabra incorrecta. El método opera en dos etapas de procesamiento. Primero, mediante la utilización de un filtro basado en trigramas se construye una lista de términos candidatos; luego, se ordena la lista utilizando la métrica distancia de edición. Los primeros resultados muestran el método basado en trigramas es una alternativa válida para la corrección de errores de ortografía, alcanzando un rendimiento cercano al 81%. Especialmente, se debe considerar que se trata de un corrector de ortografía de propósito general basado en palabras aisladas y sin ningún tipo de información del contexto.Eje: OtrosRed de Universidades con Carreras en Informática (RedUNCI

Método de corrección ortográfica basado en trigramas y distancia de edición

En este trabajo se exponen los primeros resultados obtenidos de evaluación de un método de corrección ortográfica. Éste permite identificar errores y generar una lista de posibles reemplazos ordenada de acuerdo a la distancia que las sugerencias mantienen con la palabra incorrecta. El método opera en dos etapas de procesamiento. Primero, mediante la utilización de un filtro basado en trigramas se construye una lista de términos candidatos; luego, se ordena la lista utilizando la métrica distancia de edición. Los primeros resultados muestran el método basado en trigramas es una alternativa válida para la corrección de errores de ortografía, alcanzando un rendimiento cercano al 81%. Especialmente, se debe considerar que se trata de un corrector de ortografía de propósito general basado en palabras aisladas y sin ningún tipo de información del contexto.Eje: OtrosRed de Universidades con Carreras en Informática (RedUNCI