This article critiques the anthropocentric tendencies in machine listening practices and narratives, developing alternative concepts and methods to explore the more-than-human potential of these technologies through the framework of sonic fiction. Situating machine listening within the contemporary soundscape of dataveillance, the research examines post-anthropocentric threads that emerge at the intersection of datafication, subjectivation and animalisation. Theory and practice interweave in the composition of a music piece, The Spiral, enabling generative feedback between concept, sensation and technique. Specifically, the research investigates the figure of a mollusc bio-sensor between science fact and fable, as the (im)possible locus of musicality. This emergent methodology also offers new insights for other sound art and music practices aiming to pluralise what listening might be.

Seth Kim Cohen’s notion of non-cochlear sound art explores the idea of more-than-music, reframing sonic listening, shifting away from the aesthetic and towards the conceptual, reducing ‘the value of sonic pleasure in favor of a broader set of philosophical, social, political, and historical concerns’. While this notion holds academic and artistic merit, it does not acknowledge similar explorations in sound art within disabled and d/Deaf communities and developments within disability aesthetics. Works within the disability arts that fit into Kim-Cohen’s non-cochlear sound art were created prior to the publication of his 2009 text In the Blink of an Ear: Toward a Non-Cochlear Sound Art and have continued to develop since. This article discusses Kim-Cohen’s non-cochlear sound and asks the reader to view it alongside discussions of disability aesthetics and sound art works by Hard of Hearing (HoH) and d/Deaf artists. In doing so, it illustrates how disability art and aesthetics are inherently conceptual and sociopolitical and have not only been forgotten in discussion of non-cochlear sound art, but have also carved their own path.

Biomimicry shifts focus away from anthropocentric design approaches and encourages practitioners to develop a sensitivity to the interconnectedness of natural systems and their resultant potentiality as musical forms. Embracing the concepts of biomimicry necessitates a perspectival transformation from human authorship towards a reciprocal partnership with nature that stresses sustainable technological innovation in artistic expression. The need to solve design challenges in harmony with a broader ecological context means that biomimicry represents a new form of environmentally attuned sonic practice that is both communicative and interpretative of systems operating outside everyday human experience. This research employs the biomimetic process to unravel and respond to issues related to the development of form and structure at the locus of compositional practice. Furthermore, it utilises these insights to generate new knowledge through the activities of this practice and the novel insights apprehended through the triangulation of science, nature and music. Finally, it uses biomimicry to impact the compositional trajectory practically, extending beyond metaphor or representation, and offers a glimpse into realms that are more than music, more than human.

In developing countries, a significant amount of natural gas is used for household water heating, accounting for roughly 50% of total usage. Legacy systems, typified by large water heaters, operate inefficiently by continuously maintaining a large volume of water at a constant temperature, irrespective of demand. With dwindling domestic gas reserves and rising demand, this increases dependence on expensive energy imports.

We introduce a novel Internet of Things (IoT)-inspired solution to understand and predict water usage patterns and only activate the water heater when there’s a predicted demand. This retrofit system is maintenance-free and uses a rechargeable battery powered by a thermoelectric generator (TEG), which capitalizes on the temperature difference between the heater and its environment for electricity. Our study shows a notable 70% reduction in natural gas consumption compared to traditional systems. Our solution offers a sustainable and efficient method for water heating, addressing the challenges of depleting gas reserves and rising energy costs.

We present plingo, an extension of the answer set programming (ASP) system clingo that incorporates various probabilistic reasoning modes. Plingo is based on $\textit{Lpmln}^{\pm }$, a simple variant of the probabilistic language Lpmln, which follows a weighted scheme derived from Markov logic. This choice is motivated by the fact that the main probabilistic reasoning modes can be mapped onto enumeration and optimization problems and that $\textit{Lpmln}^{\pm }$ may serve as a middle-ground formalism connecting to other probabilistic approaches. Plingo offers three alternative frontends, for Lpmln, P-log, and ProbLog. These input languages and reasoning modes are implemented by means of clingo’s multi-shot and theory-solving capabilities. In this way, the core of plingo is an implementation of $\textit{Lpmln}^{\pm }$ in terms of modern ASP technology. On top of that, plingo implements a new approximation technique based on a recent method for answer set enumeration in the order of optimality. Additionally, in this work, we introduce a novel translation from $\textit{Lpmln}^{\pm }$ to ProbLog. This leads to a new solving method in plingo where the input program is translated and a ProbLog solver is executed. Our empirical evaluation shows that the different solving approaches of plingo are complementary and that plingo performs similarly to other probabilistic reasoning systems.

Data irregularities, namely small disjuncts, class skew, imbalance, and outliers significantly affect the performance of classifiers. Another challenge posed to classifiers is when new unlabelled data have different characteristics than the training data; this change is termed as a data shift. In this paper, we focus on identifying small disjuncts and dataset shift using the supervised classifier, sequential ellipsoidal partitioning classifier (SEP-C). This method iteratively partitions the dataset into minimum-volume ellipsoids that contain points of the same label, based on the idea of Reduced Convex Hulls. By allowing an ellipsoid that contains points of one label to contain a few points of the other, such small disjuncts may be identified. Similarly, if new points are accommodated only by expanding one or more of the ellipsoids, then shifts in data can be identified. Small disjuncts are distribution-based irregularities that may be considered as being rare but more error-prone than large disjuncts. Eliminating small disjuncts by removal or pruning is seen to affect the learning of the classifier adversely. Dataset shifts have been identified using Bayesian methods, use of confidence scores, and thresholds—these require prior knowledge of the distributions or heuristics. SEP-C is agnostic of the underlying data distributions, uses a single hyperparameter, and as ellipsoidal partitions are generated, well-known statistical tests can be performed to detect shifts in data; it is also applicable as a supervised classifier when the datasets are highly skewed and imbalanced. We demonstrate the performance of SEP-C with UCI, MNIST handwritten digit image, and synthetically generated datasets.

To meet the high-precision positioning requirements for hybrid machining units, this article presents a geometric error modeling and source error identification methodology for a serial–parallel hybrid kinematic machining unit (HKMU) with five axis. A minimal kinematic error modeling of the serial–parallel HKMU is established with screw-based method after elimination of redundant errors. A set of composite error indices is formulated to describe the terminal accuracy distribution characteristics in a quantitative manner. A modified projection method is proposed to determine the actual compensable and noncompensable source errors of the HKMU by identifying such transformable source errors. Based on this, the error compensation and comparison analysis are carried out on the exemplary HKMU to numerically verify the effectiveness of the proposed modified projection method. The geometric error evaluations reveal that the parallel module has a larger impacts on the terminal accuracy of the platform of the HKMU than the serial module. The error compensation results manifest that the modified projection method can find additional compensable source errors and significantly reduce the average and maximum values of geometric errors of the HKMU. Hence, the proposed methodology can be applied to improve the accuracy of kinematic calibration of the compensable source errors and can reduce the difficulty and workload of tolerance design for noncompensable source errors of such serial–parallel hybrid mechanism.