Information retrieval in information systems (IS) with large amounts of data is not only a matter of an effective IS architecture and design and technical parameters of computer technology used for operation of the IS, but also of an easy and intuitive orientation in a number of offers and information provided by the IS. Such retrievals in IS are, however, frequently carried out with indetermin...

Language models (LMs) are often constructed by building multiple component LMs that are combined using interpolation weights. By tuning these interpolation weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. In this work, improved LM adaptation is achieved by introducing context dependent interpolation weights. An important part of th...

Most work on model-based cross-platform user interface development is based on an assumption that the user interfaces on the different platforms should be as similar as possible. Much work on mobile user interfaces claim the opposite – that user interfaces on a mobile platform should have features not applicable on a stationary one and vise versa. Exploiting contextual information in user inter...

We develop an algorithm for adapting a semantic segmentation model that is trained using labeled source domain to generalize well in unlabeled target domain. A similar problem has been studied extensively the unsupervised adaptation (UDA) literature, but existing UDA algorithms require access both data and training agnostic model. Relaxing this constraint enables user adapt pretrained models do...

In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future....

We describe a mixture-model approach to adapting a Statistical Machine Translation System for new domains, using weights that depend on text distances to mixture components. We investigate a number of variants on this approach, including cross-domain versus dynamic adaptation; linear versus loglinear mixtures; language and translation model adaptation; different methods of assigning weights; an...

The translation model of statistical machine translation systems is trained on parallel data coming from various sources and domains. These corpora are usually concatenated, word alignments are calculated and phrases are extracted. This means that the corpora are not weighted according to their importance to the domain of the translation task. This is in contrast to the training of the language...

2. PROPOSED ADAPTATION MODEL Abstract When we watch television or computer monitor under a certain viewing condition, we partially adapt to the display and partially to the ambient light. As illumination level and chromaticity changes, the eye’s white point changes between the display’s white point and the ambient light’s white point. In this paper, we propose a model that would predict the whi...