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Horse-directed vocalizations : Clicks, trills, and /ho:/
No full textThe study investigates horse-directed vocalizations in English and German. A corpus of human-horse activities contains clicks, trills, and variants of /ho:/. Horse-directed vocalizations show much phonetic and prosodic variation, which makes them adjustable to local interactional contexts. The largest group are clicks (lateral, dental, bilabial), which are used to ask horses to move faster. Trills (bilabial, alveolar) optionally end in alveolar stops. Their duration, intonation, and overall pitch vary considerably. German and English speakers use trills for opposite interactional purposes (slowing down vs. speeding up). /ho:/-type vocalizations vary with regard to first consonants, vowels, final consonants, duration, and intonation. /ho:/-variants are used to calm and/or slow horses down. Unlike non-lexical vocalizations in human talk, horse-directed vocalizations have specific, conventionalized meanings
Landscape
No full textThe rapid modernisation of the British landscape in the post-war period necessitated the positive adoption of strikingly powerful forms, particularly those of power generation, which were bound with consensual ideals of progress and technologically driven futures. The relationship of these large structures with the variety of landscapes around them was a task that needed masterful spatial planning and communication that succeeded in convincing the public about not just their necessity, but also their role in creating a vision of a new, modern countryside. Through the work of the landscape architectural profession and the nationalised industry of the CEGB, the disposition of large sculptural objects, such as cooling towers, in the landscape ensured that their commanding presence became part of the shared iconography of modernity. The need for power stations to be close to water for cooling purposes meant that many were sited in flat expanses and visible for miles, sometimes in dramatic clusters as was the case with Drax, Eggborough and Ferrybridge, that drew their water from the Rivers Aire and Ouse, upstream of their confluence. They are sentinels, signals and symbols, imbibed with senses of place, pride and purpose through their position and aspect
Climate assemblies and the public : An analysis of UK cases
No full textClimate assemblies (CAs) are being increasingly used to engage citizens in climate change policy making. Consequently, their design and operation are focused on optimising their influence on policymakers. Less emphasis is placed on how, and to what extent, CAs influence the wider public. This is an important gap as it has been suggested that CAs could stimulate public deliberation about climate change action and attitudes. Public support could also increase pressure on decision-makers to act on an assembly’s recommendations. Given the small numbers of participants typically found in CAs, implementation of their recommendations is more legitimate if broader public support is secured. Research to date has focused on citizens’ assemblies generally, rather than CAs specifically. Given the complexity and importance of the climate issue this gap needs to be addressed. Moreover, much of the existing research is based on experiments with hypothetical results. Research focused on high profile natural cases is therefore essential. To fill these gaps, we assess the relationship between CAs and the public through an analysis of Climate Assembly UK and Scotland’s Climate Assembly. These are two of the first national CAs and attracted media coverage. We conducted public opinion surveys on both cases. We find that public awareness of both CAs was low. Despite this, the idea of CAs contributing to climate policy is broadly supported by the UK and Scottish publics even by some people not concerned about climate change
States, law, and the regulation of controversial health-related claims: consolidating a research agenda between disciplines and contexts
Get PDFStories of unproven, disproven, or misleading health-related claims, and their impact on individual and public health, are commonplace around the world. Disquiet about such claims is ubiquitous and growing within public, clinical, scientific, and policy discourse, with law commonly presented as having an important role to play in addressing concerns. Action, though, requires regulators to account for competing considerations, including fundamental freedoms, cultural diversity, and the potential for law to exacerbate inequalities. The latter is particularly significant when assessing the veracity of marginalised beliefs. In practice, legal decision-makers walk a fine line between everyday tolerance and occasional intervention. Yet, legal research pertinent to these issues is surprisingly limited. Here, we argue that new knowledge, methods, and collaborations are needed to better understand how regulatory interventions relevant to contested claims are constituted; how they operate in practice; and how they relate to different political and social processes - including acts of public resistance (like campaigns and protests). Only once we are collectively equipped with such critical knowledge of the current nature and possibilities of regulatory relations will it be possible to collectively design more imaginative and inclusive legal responses
Verifiable decentralized identity-based meta-computing in Industrial Internet of Things (IIOT)
Get PDFMeta-computing in Industrial Internet of Things (IIoT) has triggered a dramatic advance due to the gigantic supports of computation power for processing complex IIoT tasks. However, users identities are encountering security and verification issues since emerging threats derive from dynamic inter-operations in the cross-organization context. Even though blockchain-based Decentralized Identity (DID) is an alternative for offering a strengthened identity governance, current verifiability of DID documents still encounters vulnerabilities due to the involvement of the less trustful third parties that maintain the storage of binding relationships between DID identifiers and public keys. In this paper, we propose a novel Verifiable and Searchable Decentralized Identity (VS-DID) model. We focus on the verifiability of DID documents and propose a verifiable registry scheme that ensures verifiable binding relationships. In order to enable efficient queries in large-scale users’ identities in meta-computing IIoT, we develop an on-chain-off-chain query strategy that adopts a slide window accumulator. The experimental results show that our scheme reduces aggregate proof time and commitment time by 93.5% and 96.5%, respectively, compared to the Merkle SNARK scheme, while maintaining reasonable verification time, significantly improving the efficiency of DID registry in large-scale IIoT environments
Lightweight Continuous Authentication via IMU Fingerprinting for V2X
Get PDFInertial measurement unit (IMU) fingerprinting is a promising physical authentication technique based on hardware imperfections produced during sensor manufacturing. This paper presents a two-stage feature extraction process that combines feature selection and mapping; the proposed approach is tailored for the lightweight vehicle-to-everything (V2X) application scenario. Specifically, the selected features are transformed into images via Gramian angular difference field (GADF), Gramian angular summation field (GASF), and Markov transition field (MTF) mappings, as well as feature extraction implemented via a convolutional neural network (CNN). Owing to the advances provided by the proposed scheme, a lightweight feature extraction system achieves satisfactory accuracy levels above 99.10% with fewer sample data and a short training time. The effectiveness and robustness of the developed approach were validated under various driving conditions via 20 IMU sensors, Arduino, and a Raspberry Pi across 20 vehicles. Additionally, tests conducted across different deep learning models demonstrated the generalizability of the proposed preprocessing and mapping methods
SCTP : Achieving Semantic Correlation Trajectory Privacy-Preserving With Differential Privacy
Get PDFWith the rapid proliferation of vehicular technology, location-based services (LBS) have become a crucial component of Internet of Vehicles (IoV) applications, such as map navigation and health tracking. These applications rely on users' location information to provide services, enabling users to effectively share their locations, access information about nearbyactivities, and engage in real-time communication. However, the extensive collection and sharing of location data pose serious challenges to the semantic privacy preservation of user locations. To address these challenges in IoV, we propose a Semantic Correlation Trajectory Privacy-Preserving mechanism (SCTP). The SCTP combines the Hidden Markov Models (HMM) with differential privacy, aiming to protect the semantic privacy of user trajectory locations while maintaining high-quality location services and data usability. Our scheme introduces a trajectory prediction algorithm based on HMM, which dynamically and accurately predicts user trajectories and generates highly available semantically correlated trajectory datasets. Additionally, we design a personalized privacy budget allocation strategy based on semantic frequency. By assigning privacy weights, we significantly improve the usability of trajectory data while protecting data privacy. Theoretical analysis and experimental validation demonstrate that SCTP rigorously adheres to ε-differential privacy standards while exhibiting significant advantages in safeguarding the semantic privacy of user locations
EASTER : Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning
Get PDFVertical Federated Learning (VFL) allows collaborative machine learning without sharing local data. However, existing VFL methods face challenges when dealing with heterogeneous local models among participants, which affects optimization convergence and generalization of participants' local knowledge aggregation. To address this challenge, this paper proposes a novel approach called Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning (EASTER). EASTER focuses on aggregating the local embeddings of each participant's knowledge during forward propagation. We propose an embedding protection method based on lightweight blinding factors, which injects the blinding factors into the local embedding of the passive party. However, the passive party does not own the sample labels, so the local model's gradient cannot be calculated locally. To overcome this limitation, we propose a new method in which the active party assists the passive party in computing its local heterogeneous model gradients. Theoretical analysis and extensive experiments demonstrate that EASTER can simultaneously train multiple heterogeneous models and outperform some recent methods in model performance. For example, compared with the state-of-the-art method, the model accuracy of EASTER was improved by 7.22% under the CIFAR-10 dataset