Adaptive psychometric scaling for video quality assessment

Video quality estimation is crucial for the efficient management of video delivery services. Particularly with the advances in screen technology and content delivery networks, getting an accurate estimation of the video quality as it is actually perceived by the user, is a key factor in delivering high quality-of-experience. Psychometric scaling provides the tools to measure the impact that different types of impairments have on the delivered quality. In contrast to the more conventional subjective rating procedures, psychometric scaling does not suffer from biases and has significantly lower variability. However, the existing psychometric methods such as Maximum Likelihood Difference Scaling (MLDS) entail a large number subjective tests. Herein we present an adaptive approach that leads to improvement in the learning rate and, in turn, to resource-efficient video delivery systems.     

Analysis of fly ash by X-ray emission spectroscopy and proton microbeam analysis

The use of X-ray emission spectroscopy for the study of properties of fly-ash particles is reported where different techniques of sample excitation were used. The elemental composition of bulk ash was obtained by exposing the samples to radiation from radioactive sources, X-ray tube and synchrotron radiation. Proton microbeam was used to measure the concentration profiles of different elements in individual ash particles.     

Efficient and effective training of sparse recurrent neural networks

Neural Computing and Applications - Recurrent neural networks (RNNs) have achieved state-of-the-art performances on various applications. However, RNNs are prone to be memory-bandwidth limited in...     

Intelligent tutoring interoperability for the new web

Interoperability of systems based on knowledge is a very important element for reducing their development cost and enabling an easy-to-perform service enrichment. Intelligent tutoring systems (ITSs) may be described as distant learning systems, which base their work on the simulation of the “real” teacher in the learning and teaching process. ITSs base their interoperability on the interchange of domain knowledge, knowledge about learning and teaching process and knowledge about students. This paper describes DiSNeT, a distance learning system we designed based on the intelligent tutoring paradigm, on knowledge presentation using distributed semantic networks and on using agents in the learning and teaching process. We also present a methodology for ensuring interoperability between DiSNeT and other ITSs.     

n-gram-based classification and unsupervised hierarchical clustering of genome sequences

In this paper we address the problem of automated classification of isolates, i.e., the problem of determining the family of genomes to which a given genome belongs. Additionally, we address the problem of automated unsupervised hierarchical clustering of isolates according only to their statistical substring properties. For both of these problems we present novel algorithms based on nucleotide n-grams, with no required preprocessing steps such as sequence alignment. Results obtained experimentally are very positive and suggest that the proposed techniques can be successfully used in a variety of related problems. The reported experiments demonstrate better performance than some of the state-of-the-art methods. We report on a new distance measure between n-gram profiles, which shows superior performance compared to many other measures, including commonly used Euclidean distance.     

Digging Deep into the Data Mine with DataMiningGrid

The growing computerization in modern knowledge and technology sectors is generating huge volumes of electronically stored data. Data mining technology is often employed to make sense of these data. However, as modern data mining applications increase in complexity, so do their demands for resources. Grid computing is one of several emerging networked computing paradigms promising to meet the requirements of heterogeneous, large-scale and distributed data mining applications. Despite this promise, there are still too many issues to be resolved before grid technology is commonly applied to large-scale data mining tasks. To address some of these issues, we developed the DataMiningGrid system, which principally differs from similar systems by its ability to integrate a diverse set of programs and application scenarios within a single framework. The system's key features include high performance and scalability, sophisticated support for relevant standards, different user types, and flexible extensibility. The software is available as open source.     

Eigenvalues Driven Gaussian Selection in continuous speech recognition using HMMs with full covariance matrices

In this paper a novel algorithm for Gaussian Selection (GS) of mixtures used in a continuous speech recognition system is presented. The system is based on hidden Markov models (HMM), using Gaussian mixtures with full covariance matrices as output distributions. The purpose of Gaussian selection is to increase the speed of a speech recognition system, without degrading the recognition accuracy. The basic idea is to form hyper-mixtures by clustering close mixtures into a single group by means of Vector Quantization (VQ) and assigning it unique Gaussian parameters for estimation. In the decoding process only those hyper-mixtures which are above a designated threshold are selected, and only mixtures belonging to them are evaluated, improving computational efficiency. There is no problem with the clustering and evaluation if overlaps between the mixtures are small, and their variances are of the same range. However, in real case, there are numerous models which do not fit this profile. A Gaussian selection scheme proposed in this paper addresses this problem. For that purpose, beside the clustering algorithm, it also incorporates an algorithm for mixture grouping. The particular mixture is assigned to a group from the predefined set of groups, based on a value aggregated from eigenvalues of the covariance matrix of that mixture using Ordered Weighted Averaging operators (OWA). After the grouping of mixtures is carried out, Gaussian mixture clustering is performed on each group separately.