This paper proposes a zero-shot speech emotion recognition (SER) method that estimates emotions not previously defined in the SER model training. Conventional methods are limited to recognizing emotions defined by a single word. Moreover, we have the motivation to recognize unknown bipolar emotions such as ``I want to buy - I do not want to buy.'' In order to allow the model to define classes using sentences freely and to estimate unknown bipolar emotions, our proposed method expands upon the contrastive language-audio pre-training (CLAP) framework by introducing multi-class and multi-task settings. We also focus on purchase intention as a bipolar emotion and investigate the model's performance to zero-shot estimate it. This study is the first attempt to estimate purchase intention from speech directly. Experiments confirm that the results of zero-shot estimation by the proposed method are at the same level as those of the model trained by supervised learning.

However, they assume that users have knowledge of the total number of users occupying their channel at any given time. Dynamic spectrum access has emerged to address the problem of spectrum under-utilization caused by treating the frequency On any given channel, we assume that the reward obtained spectrum as a fixed commodity. We study the spectrum is a random variable that is drawn from a distribution that sharing paradigm in which all the users are treated equally i.e., depends on the number of users on the channel. For example, there is no distinction between primary or secondary users. We the instantaneous reward could be the rate achieved by the user model the system as a stochastic multi-user multi-armed bandit on the channel which may decrease due to interference from (MAB) problem [1] where the channels correspond to the other users accessing the channel. The decrease in the reward arms of the bandit similar to the model considered in [2]-[11].

The existing spectrum management paradigm treats frequency spectrum as a fixed commodity, which leads to spectrum under utilization. Cognitive radio has emerged as a useful strategy to increase spectrum utilization. The existing literature on cognitive radio has largely been focused on the primary/secondary user paradigm, where secondary users need to detect vacant spectrum when available and vacate the occupied spectrum when a primary user wants to transmit. We focus on a different type of spectrum sharing system in which there is no distinction between users, and in which there is no coordination among the users. The collective performance across all users is more important than that of individual users. This is in contrast to the typical primary/secondary user paradigm in which secondary users bear the responsibility for ensuring priority-based spectrum sharing.

Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.