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How to navigate safely, recognize encountered obstacles, and move independently from one location to another in unknown environments are some of the challenges that face visually impaired people. By proposing a solution towards overcoming these challenges, this work will be of most importance to visually impaired people. In this work, we propose a consistent, reliable and robust smartphone-based method to classify obstacles in unknown environments from partial visual information based on computer vision and machine learning techniques. Our proposed method handles high levels of noise and bad resolution in frames captured from a phone camera. In addition, our proposed method offers maximum flexibility to users and use the least expensive equipment possible. Moreover, by leveraging on deep-learning techniques, the proposed method enables semantic categorization in order to classify obstacles and increase the awareness of the explored environment. The efficiency of the work has been experimentally measured on a variety of experiments studies on different complex scenes. It records high accuracy of [90.2 % ]. INDEX TERMS Image analysis, image classification, supervised learning, mobile applications.

Abstract-Business Processes naturally involve long running activities and require transactional behaviour across them. The work presented in this paper is a proposal for a novel autonomous failure handling mechanism for long running nested transactions (LRT) and forms part of a general management and compensation model for long running transactions in workflows. The mechanism is based on propagation of failures through a recursive hierarchical structure of transaction components (nodes and execution paths). The management system of transactions (COMPMOD) is implemented as a reactive system controller, where system components change their states based on rules in response to triggering of events such as activation, failure, force-fail, completion, or compensation events. A notable new feature of the model is the distinction of vital and non-vital components, allowing the process designer to express the cruciality of activities in the workflow with respect to the business logic.Keywords-LRT, compensation and failure handling, rule based reactive approach.

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Estimating translation quality is a problem of growing importance as it has many potential applications. The quality of translation from Arabic to English is especially difficult to evaluate due to the languages being distant languages: different in syntax and low in lexical similarity. We propose a feature-based framework for estimating the quality of Arabic to English translations at the sentence level. The proposed method works without reference translations, considers both fluency and adequacy of translations, and does not imply assumptions on the source of translation (humans, machines, or post-edited machine translations); thus, making the solution applicable to increasingly more situations. This research solves the translation quality estimation problem by treating it as a supervised machine learning problem. The proposed model utilizes regression algorithms (SVR and Linear Regression) to predict quality scores of unseen translated texts at runtime. This is accomplished by training models on a labeled parallel corpus and mapping extracted features to the quality label. The prediction models succeeded in predicting fluency and adequacy of translations with a Mean Absolute Error of 0.84 and 1.02, respectively. Furthermore, we show that in a similar setting of our approach, fluency of an Arabic to English translated sentence on its own, is an appropriate indication of a translation's overall quality.