The assurance of quality of experience (QoE) and provisioning of high throughput of the system represent the main goals of future wireless and mobile networks. This paper presents novel and practical cross-layer QoE-aware radio resource allocation (RRA) algorithms for the downlink of a heterogeneous orthogonal frequency division multiple access (OFDMA) system. The objective of the proposed algorithms is to assure the appropriate level of QoE for each user of the system by incorporating application-layer parameters and subjective human perception of quality into the RRA process. We propose two user-oriented joint subcarrier and power allocation algorithms with low complexity for real-time and interactive services. The first algorithm dynamically allocates resources by assuring the same level of QoE to all users of the system, whereas the second algorithm introduces the efficient trade-off between the user's QoE and the spectral efficiency of the system. By considering application-layer parameters and user's perception of quality, high users' QoE and explicit control of data rates can be achieved. The numerical results show that the proposed algorithms achieve significant increase in the level of QoE compared to previous works, a fair distribution of capacity among users and near to optimal solution of QoE for the OFDMA system.

We consider similarity-based relational databases that allow to retrieve approximate data, find data within a given range of distance or similarity, and support imprecise queries. We focus on the recently introduced relational algebra with similarities on [Formula: see text]-relations, which are annotated with multi-dimensional similarity values with each dimension referring to a single attribute. The codomains of the annotated relations are De Morgan frames, and the annotations express the relevance of the tuples as answers to a similarity-based query. In this paper, we study Datalog programs on [Formula: see text]-relations, with and without negation. We describe the least-fixpoint algorithm for safe and rectified Datalog programs on [Formula: see text]-relations with finite support but without negative literals in the body. We further describe the perfect-minimal-fixpoint algorithm of a Datalog program on [Formula: see text]-relations with finite support and negative literals in the body when rules are safe, rectified and stratified. We introduce the idea of controlling the calculation of the annotations such that the tuples that enter an IDB relation last will be announced less desirable than those that enter first. For this we define a damping function that augments/diminishes the individual annotations that contribute to the final annotations of tuples. With a damping function, for instance, long chains of inferences may be made significantly less desirable or even totally undesirable.

In this paper we propose various extensions to the relational model to support similarity-based querying. We build upon the -relation model, where tuples are assigned values from an arbitrary semiring , and its associated positive relational algebra $\text{RA}^{+}_{\mathcal{K}}$ . We consider a recently proposed extension to $\text{RA}^{+}_{\mathcal{K}}$ using a monus operation on the semiring to support negative queries, and show how, surprisingly, it fails for important ‘fuzzy’ semirings. Instead, we suggest using a negation operator. We also consider the identities satisfied by the relational algebra $\text{RA}^{+}_{\mathcal{K}}$ . We show that moving from a semiring to a particular form of lattice (a De Morgan frame) yields a relational algebra that satisfies all the classical (positive) relational algebra identities. We claim that to support real-world similarity queries realistically, one must move from tuple-level annotations to attribute-level annotations. We show in detail how our De Morgan frame-based model can be extended to support attribute-level annotations and give worked examples of similarity queries in this setting.

There has been a great deal of interest over the past 20 years in developing metrics and frameworks for evaluating and comparing the performance of spoken-language dialogue systems. One of the results of this interest is a potential general methodology, known as the PARADISE framework. This squib highlights some important issues concerning the application of PARADISE that have, up to now, not been sufficiently emphasized or have even been neglected by the dialogue-system community. These include considerations regarding the selection of appropriate regression parameters, normalization effects on the accuracy of the prediction, the influence of speech-recognition errors on the performance function, and the selection of an appropriate user-satisfaction measure. In addition, it gives the results of an evaluation of data from two Wizard-of-Oz experiments. These evaluations include different dependent variables and examination of individual user-satisfaction measures.