Traffic Sign Detection Based on Convolutional Neural Network

In today's world, nearly everything we have a tendency to do has been simplified by machine-driven tasks. In a trial to specialize in the road whereas driving, drivers usually miss out on signs on the facet of the road, that can be dangerous for them and for the folks around them. This drawback may be avoided if there was AN economical thanks to inform the motive force while not having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays a vital role here by detection and recognizing a symptom, therefore notifying the motive force of any coming signs. This not solely ensures road safety, however additionally permits the motive force to be at very little a lot of ease whereas driving on tough or new roads. Another normally long-faced drawback isn't having the ability to know the which means of the sign. With the assistance of this Advanced Driver help Systems (ADAS) application, drivers can not face the matter of understanding what the sign says. during this paper, we have a tendency to propose a way for Traffic Sign Detection and Recognition exploitation image process for the detection of a symptom and a Convolutional Neural Networks (CNN) for the popularity of the sign. CNNs have a high recognition rate, therefore creating it fascinating to use for implementing varied laptop vision tasks. TensorFlow is employed for the implementation of the CNN. Keywords: actitvity recognition; knowledge collection; knowledge preprocessing; coaching CNN model ;evaluating model; predicting the result.

Language and Brand

The language used in a product or service has an extraordinary impact on the creation of its brand and on its online success. As localization is a key aspect of a globalized business, attention should be given to the localization of brand language to ensure global consistency. This study explores brand language localization problems in an online help corpus. Specifically, it analyzes the problems posed by the localization of brand names and terms in the pre-translation phase, following Nord’s pre-translation text analysis theory (2012). The main objective of the study is to understand the nature of identified brand language problems (professional purposes) and examine them (research purposes). The method implemented is a qualitative, interpretative analysis of a monolingual corpus in English comprising representative extracts from the Dropbox and Google Drive Online Help systems. The study is part of a wider research project exploring the concept of localization problems in online help localization.

Help through demonstration and automation for interactive computing systems: A survey of recent works

Usability is very important however, it is still difficult to develop interactive computing systems that meet all user’s specificities. Help systems should be a way of bridging this gap. This paper presents a general survey on recent works (building upon previous surveys) related to improving applications’ help through demonstration and automation and, identifies which technologies are acting as enablers. The main contributions are, identifying (i) which are the recent existing solutions; (ii) which aspects must be investigated further; and (iii) which are the main difficulties that are preventing a faster progress.

Troubleshooters for Tasks of Introductory Programming MOOCs

Learning programming has become more and more popular and organizing introductory massive open online courses (MOOCs) on programming can be one way to bring this education to the masses. While programming MOOCs usually use automated assessment to give feedback on the submitted code, the lack of understanding of certain aspects of the tasks and feedback given by the automated assessment system can be one persistent problem for many participants. This paper introduces troubleshooters, which are help systems, structured like decision trees, for giving hints and examples of certain aspects of the course tasks. The goal of this paper is to give an overview of usability (benefits and dangers) of, and the participants’ feedback on, using troubleshooters. Troubleshooters have been used from the year 2016 in two different programming MOOCs for adults in Estonia. These MOOCs are characterized by high completion rates (50–70%), which is unusual for MOOCs. Data is gathered from the learning analytics integrated into the troubleshooters’ environment, letters from the participants, questionnaires, and tasks conducted through the courses. As it was not compulsory to use troubleshooters, the results indicate that only 19.8% of the users did not use troubleshooters at all and 10% of the participants did not find troubleshooters helpful at all. The main difference that appeared is that the number of questions asked from the organizers about the programming tasks during the courses via helpdesk declined about 29%.

Interfacing informal and formal help systems: Historical pathways to the Meitheal model

Meitheal is a national practice model which aims to ensure that the needs and strengths of children and their families are effectively identified, understood and responded to in a timely manner. The aim of this article is to consider some of the notable learning from the historical background and context in the development of children and family services. The discussion draws together four interrelated themes: the interaction between the voluntary and statutory systems, the interface of family and child oriented services, balancing formal and informal responses to child welfare, and early intervention and prevention services. The complexity of this endeavour is emphasised through identifying the core considerations required at the levels of Bronfenbrenner’s ecological model. The article concludes with a commentary on how the future of child welfare in Ireland may be influenced through this attempt at a reorientation of children and family services towards early intervention, prevention, partnership and participation.