Abstract Underwater environment is one of the most challenging areas when considering scene reconstruction and mapping, feature extraction etc. GPS usage denied in underwater a waterproof lidar also not cost-effective solution to map localize. Lidar-based simultaneous localization mapping (SLAM) are preferred budget-constrained applications. So, visual-slam (V-SLAM) approach called Real-Time Ap...

Smart manufacturing in the “Industry 4.0” strategy promotes deep integration of and information technologies, which makes system a ubiquitous environment. However, real-time scheduling such is challenge faced by many decision makers. To deal with this challenge, study focuses on hybrid flow shop problem (HFSP). First, characteristic smart environment analyzed, its described. Secon...

This paper describes the development of a multimedia information server that supports state-of-the-art distributed applications. This is achieved through techniques for efficient storage management of large objects, the provision of real-time data pumps controlled by scheduling mechanisms, multimedia object modelling and ODBMS functionality for the manipulation of objects. The system delivers M...

We present APRO, a new method for machine translation tuning that can handle large feature sets. As opposed to other popular methods (e.g., MERT, MIRA, PRO), which involve randomness and require multiple runs to obtain a reliable result, APRO gives the same result on any run, given initial feature weights. APRO follows the pairwise ranking approach of PRO (Hopkins and May, 2011), but instead of...

The log-linear combination of different features is an important component of SMT systems. It allows for the easy integartion of models into the system and is used during decoding as well as for nbest list rescoring. With the recent success of more complex models like neural network-based translation models, n-best list rescoring attracts again more attention. In this work, we present a new tec...

3D Collaborative Virtual Environments (CVEs) or virtual worlds have been widely used in educational settings for the purposes of simulation and demonstration of scientific concepts, art and historical events that for practical reasons may be complicated in real-life classrooms. This paper describes an experience of recreating a central event in Norwegian history, adoption of Norwegian constitut...

Minimum error rate training (MERT) involves choosing parameter values for a machine translation (MT) system that maximize performance on a tuning set as measured by an automatic evaluation metric, such as BLEU. The method is best when the system will eventually be evaluated using the same metric, but in reality, most MT evaluations have a human-based component. Although performing MERT with a h...

Currently available algorithms for data stream classification are all designed to handle precise data, while data with uncertainty or imperfection is quite natural and widely seen in real-life applications. Uncertainty can arise in attribute values as well as in class values. In this paper, we focus on the classification of streaming data that has different degrees of uncertainty within class v...