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The creative and cultural sectors in the United Kingdom largely exclude the working classes. Even the small number of working-class people who do ‘make it’ into these sectors often find themselves and their work badly treated by those who hold the real power. This article explores some of the experiences of working-class artists navigating the cultural sector and how exclusion, prejudice and precarity impacted and continue to impact them. It takes as its focus the filmmaker Alan Clarke and the playwright Andrea Dunbar, who were at the height of their success in the 1980s. It also considers the writers Darren McGarvey and Nathalie Olah, whose work has achieved prominence in recent years. It is through this focus I hope to demonstrate the long continuum of challenges for working-class creatives. This article also considers how, on the occasions when they are allowed the space they deserve, working-class artists have created powerful shifts in cultural production. Finally, it details some of the changes needed for working-class people to be able to take their rightful place in contributing to cultural life and the societal risks involved if they are denied that place.

ABSTRACT These notes aim to revisit the debate, the model, the results, and main objections to the validity of the Ricardian Equivalence Theorem as presented in Barro (1974). It is intended to explore his thesis that tax and debt are equivalent and have no real effect on perceived wealth, demand, the real interest rate or on the economy. The thesis refers to the analysis of the ways of financing debt at a given level of government expenditure and does not address the effects of an expansion of this volume of spending, nor it specifically analyzes the effects of an increase in public debt due to a tax reduction policy. After this presentation, the thesis is debated, consolidating some of the premises that are necessary to validate it. The purpose of the paper is to explore the first round of debates on the theme, explaining the restrictions to which the Barro-Ricardo Theorem or the Ricardian Equivalence Theorem is subject, based on the publications by Barro (1976), Buchanan (1976) and Feldstein (1976), all of them within the ‘realm’ of economic orthodoxy. The final section presents some remarks and an analysis of Barro’s later work (1989 and 1996).

In this article, we study the task of user profiling in question answering communities (QACs). Previous user profiling algorithms suffer from a number of defects: they regard users and words as atomic units, leading to the mismatch between them; they are designed for other applications but not for QACs; and some semantic profiling algorithms do not co-embed users and words, leading to making the affinity measurement between them difficult. To improve the profiling performance, we propose a neural Flow-based Constrained Co-embedding Model, abbreviated as FCCM. FCCM jointly co-embeds the vector representations of both users and words in QACs such that the affinities between them can be semantically measured. Specifically, FCCM extends the standard variational auto-encoder model to enforce the inferred embeddings of users and words subject to the voting constraint, i.e., given a question and the users who answer this question in the community, representations of the users whose answers receive more votes are closer to the representations of the words associated with these answers, compared with representations of whose receiving fewer votes. In addition, FCCM integrates normalizing flow into the variational auto-encoder framework to avoid the assumption that the distributions of the embeddings are Gaussian, making the inferred embeddings fit the real distributions of the data better. Experimental results on a Chinese Zhihu question answering dataset demonstrate the effectiveness of our proposed FCCM model for the task of user profiling in QACs.

Authorship attribution refers to examining the writing style of authors to determine the likelihood of the original author of a document from a given set of potential authors. Due to the wide range of authorship attribution applications, a plethora of studies have been conducted for various Western, as well as Asian, languages. However, authorship attribution research in the Urdu language has just begun, although Urdu is widely acknowledged as a prominent South Asian language. Furthermore, the existing studies on authorship attribution in Urdu have addressed a considerably easier problem of having less than 20 candidate authors, which is far from the real-world settings. Therefore, the findings from these studies may not be applicable to the real-world settings. To that end, we have made three key contributions: First, we have developed a large authorship attribution corpus for Urdu, which is a low-resource language. The corpus is composed of over 2.6 million tokens and 21,938 news articles by 94 authors, which makes it a closer substitute to the real-world settings. Second, we have analyzed hundreds of stylometry features used in the literature to identify 194 features that are applicable to the Urdu language and developed a taxonomy of these features. Finally, we have performed 66 experiments using two heterogeneous datasets to evaluate the effectiveness of four traditional and three deep learning techniques. The experimental results show the following: (a) Our developed corpus is many folds larger than the existing corpora, and it is more challenging than its counterparts for the authorship attribution task, and (b) Convolutional Neutral Networks is the most effective technique, as it achieved a nearly perfect F1 score of 0.989 for an existing corpus and 0.910 for our newly developed corpus.

Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the users’ visiting preference probabilities from the historical check-ins in batch. However, in practice, when users’ behaviors are updated in real-time, it is often cost-inhibitive to re-estimate and updates users’ visiting preference using the same batch methods due to the number of check-ins. Moreover, an important nature of users’ movement patterns is that users are more attracted to an area where have dense locations with same categories for conducting specific behaviors. In this paper, we propose a location recommendation method called GeoRTGA by utilizing the real time user behaviors and geographical attractions to tackle the problems. GeoRTGA contains two sub-models: real time behavior recommendation model and attraction-based spatial model. The real time behavior recommendation model aims to recommend real-time possible behaviors which users prefer to visit, and the attraction-based spatial model is built to discover the category-based spatial and individualized spatial patterns based on the geographical information of locations and corresponding location categories and check-in numbers. Experiments are conducted on four public real-world check-in datasets, which show that the proposed GeoRTGA outperforms the five existing location recommendation methods.

Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency . CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.

The paper addresses the driving dynamics of emergency fire trucks. It focuses on the issues of braking, measuring real braking distances and calculating adhesion coefficients. It presents the results of measuring the real braking distances for firefighting vehicles - type water tenders. Measurements were taken on dry asphalt and depending on the vehicle's speed of travel. Experiments were conducted on five types of firefighting vehicles. The results of measurements are mainly used by fire protection units to the drivers’ self-education to improve safety when driving to intervene.