Conversational Recommender Systems (CRSs) are receiving growing research attention across domains, yet their user experience (UX) evaluation remains limited. Existing reviews largely overlook empirical UX studies, particularly in adaptive and large language model (LLM)-based CRSs. To address this gap, we conducted a systematic review following PRISMA guidelines, synthesising 23 empirical studies published between 2017 and 2025. We analysed how UX has been conceptualised, measured, and shaped by domain, adaptivity, and LLM. Our findings reveal persistent limitations: post hoc surveys dominate, turn-level affective UX constructs are rarely assessed, and adaptive behaviours are seldom linked to UX outcomes. LLM-based CRSs introduce further challenges, including epistemic opacity and verbosity, yet evaluations infrequently address these issues. We contribute a structured synthesis of UX metrics, a comparative analysis of adaptive and nonadaptive systems, and a forward-looking agenda for LLM-aware UX evaluation. These findings support the development of more transparent, engaging, and user-centred CRS evaluation practices.

Arts, creative and media groups have demanded the government rule out allowing big tech companies to take Australian content to train their artificial intelligence models, with concerns such a shift would "sell out" Australian workers and lead to "rampant theft" of intellectual property. "It is not appropriate for big tech to steal the work of Australian artists, musicians, creators, news media, journalism, and use it for their own ends without paying for it," Ley said on Wednesday. In an interim report on "harnessing data and digital technology", the Productivity Commission set out proposals for how tech, including AI, could be regulated and treated in Australia, suggesting it could boost productivity by between 0.5% and 13% over the next decade, adding up to 116bn to Australia's GDP. The commission suggested several possible remedies, including expanding licensing schemes, or an exemption for "text and data mining" and expanding the existing fair dealing rules, which it said existed in other countries. The latter suggestion prompted fierce pushback from arts, creative and media companies, which raised alarm their work could be left open for massively wealthy tech companies to use – without compensation or payment – to train AI models.

An extension of the OCLUST algorithm to the functional setting is proposed to address these issue s. The approach leverages the OCLUST framework, creating a robust method to cluster cu rves and trim outliers. The methodology is evaluated on both simulated and real-wor ld functional datasets, demonstrating strong performance in clustering and outlie r identification.

--The dual formulation of empirical risk minimization with f -divergence regularization (ERM-f DR) is introduced. The solution of the dual optimization problem to the ERM-f DR is connected to the notion of normalization function introduced as an implicit function. This dual approach leverages the Legendre-Fenchel transform and the implicit function theorem to provide a nonlinear ODE expression to the normalization function. Furthermore, the nonlinear ODE expression and its properties provide a computationally efficient method to calculate the normalization function of the ERM-f DR solution under a mild condition. Empirical risk minimization (ERM) [1]-[6] is often posed as an optimization problem regularized by a statistical distance between the probability measure to be optimized and a given reference measure [7]-[13].

We propose a novel perspective on varied-density clustering for high-dimensional data by framing it as a label propagation process in neighborhood graphs that adapt to local density variations. Our method formally connects density-based clustering with graph connectivity, enabling the use of efficient graph propagation techniques developed in network science. To ensure scalability, we introduce a density-aware neighborhood propagation algorithm and leverage advanced random projection methods to construct approximate neighborhood graphs. Our approach significantly reduces computational cost while preserving clustering quality. Empirically, it scales to datasets with millions of points in minutes and achieves competitive accuracy compared to existing baselines.

Breakthroughs, discoveries, and DIY tips sent every weekday. Judging by newly released photos and video, the crew aboard Ocean Exploration Trust's Nautilus research vessel had an extremely productive summer trip to the South Pacific. Over 22 days, the team completed detailed archaeological surveys of more than a dozen shipwrecks sunk amid the Solomon Islands campaign during World War II. In addition to imaging four of them for the first time, experts guided remotely operated vehicles (ROVs) towards the rediscovery of two long-lost vessels:the separated bow from the USS New Orleans as well as the Imperial Japanese Naval destroyer Teruzuki. Although researchers originally spotted some of these shipwrecks more than 34 years ago, Ocean Exploration Trust president Robert Ballard explained that the most recent trip to Iron Bottom Sound provided opportunities to document their finds using a new generation of technology including high-definition survey cameras, underwater vehicles, and imaging tools aboard the EV Nautilus.

Biomedical decision making involves multiple signal processing, either from different sensors or from different channels. In both cases, information fusion plays a significant role. A deep learning based electroencephalogram channels' feature level fusion is carried out in this work for the electroencephalogram cyclic alternating pattern A phase classification. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms, namely, Genetic Algorithm and Particle Swarm Optimization. The developed methodologies were evaluated by fusing the information from multiple electroencephalogram channels for patients with nocturnal frontal lobe epilepsy and patients without any neurological disorder, which was significantly more challenging when compared to other state of the art works. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels, which is in line with the CAP protocol to ensure multiple channels' arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result which is in the upper range of the specialist agreement. The proposed approach is still in the upper range of the best state of the art works despite a difficult dataset, and has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models revealed to be noise resistant and resilient to multiple channel loss.

To reach human level intelligence, learning algorithms need to incorporate causal reasoning. But identifying causality, and particularly counterfactual reasoning, remains an elusive task. In this paper, we make progress on this task by utilizing instrumental variables (IVs). IVs are a classic tool for mitigating bias from unobserved confounders when estimating causal effects. While IV methods have been extended to non-separable structural models at the population level, existing approaches to counterfactual prediction typically assume additive noise in the outcome. In this paper, we show that under standard IV assumptions, along with the assumptions that latent noises in treatment and outcome are strictly monotonic and jointly Gaussian, the treatment-outcome relationship becomes uniquely identifiable from observed data. This enables counterfactual inference even in non-separable models. We implement our approach by training a normalizing flow to maximize the likelihood of the observed data, demonstrating accurate recovery of the underlying outcome function. We call our method Flow IV .

Multi-class classification annotations have significantly advanced AI applications, with truth inference serving as a critical technique for aggregating noisy and biased annotations. Existing state-of-the-art methods typically model each annotator's expertise using a confusion matrix. However, these methods suffer from two widely recognized issues: 1) when most annotators label only a few tasks, or when classes are imbalanced, the estimated confusion matrices are unreliable, and 2) a single confusion matrix often remains inadequate for capturing each annotator's full expertise patterns across all tasks. To address these issues, we propose a novel confusion-matrix-based method, PTBCC (ProtoType learning-driven Bayesian Classifier Combination), to introduce a reliable and richer annotator estimation by prototype learning. Specifically, we assume that there exists a set $S$ of prototype confusion matrices, which capture the inherent expertise patterns of all annotators. Rather than a single confusion matrix, the expertise per annotator is extended as a Dirichlet prior distribution over these prototypes. This prototype learning-driven mechanism circumvents the data sparsity and class imbalance issues, ensuring a richer and more flexible characterization of annotators. Extensive experiments on 11 real-world datasets demonstrate that PTBCC achieves up to a 15% accuracy improvement in the best case, and a 3% higher average accuracy while reducing computational cost by over 90%.

--In machine learning, the process of feature selection involves finding a reduced subset of features that captures most of the information required to train an accurate and efficient model. This work presents FeatureCuts, a novel feature selection algorithm that adaptively selects the optimal feature cutoff after performing filter ranking. Evaluated on 14 publicly available datasets and one industry dataset, FeatureCuts achieved, on average, 15 percentage points more feature reduction and up to 99.6% less computation time while maintaining model performance, compared to existing state-of-the-art methods. When the selected features are used in a wrapper method such as Particle Swarm Optimization (PSO), it enables 25 percentage points more feature reduction, requires 66% less computation time, and maintains model performance when compared to PSO alone. The minimal overhead of FeatureCuts makes it scalable for large datasets typically seen in enterprise applications. Traditional machine learning methods work best when their prediction signals come from data with a small, but highly informative set of features.