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From documents to dialogue: Context matters in common sense-enhanced task-based dialogue grounded in documents
Get PDFHumans can engage in a conversation to collaborate on multi-step tasks and divert briefly to complete essential sub-tasks, such as asking for confirmation or clarification, before resuming the overall task. This communication is necessary as some knowledge in instructional documents can be implicit rather than grounded in the dialogue, meaning that people must rely on their own and others’ knowledge for problem-solving. We often attribute this capability to common sense, i.e., the assumption that interlocutors perceive behaviours, temporality, context, space and object properties in a similar way. To explore the significance of emulating such problem-solving capabilities, we developed a novel hybrid document-grounded dialogue system (DGDS) called ChefBot1 leveraging the contextual understanding of a pre-trained language model and the structuring of a sequence-to-sequence model trained on a series of commonsense knowledge databases. In a human evaluation, the hybrid system proved more effective in capturing object knowledge (utility, appearance, storage, relationships, handling) and contextual knowledge (understanding of events and situations) compared to a rule-based baseline. A key finding of this paper is demonstrating how inferring context from different document sources enhances the dialogue by allowing richer and more fluid interaction. To our knowledge, this research is innovative in its scope as the first effort to model task-based dialogue grounded in commonsense knowledge across multiple documents
Is it real or not? construction of meaning and identity in virtual influencer marketing
Get PDFVirtual influencers (VIs) are an emerging category of social media influencer. Through a sequential mixed-method research design, this research employs the theoretical lens of symbolic interactionism to explain how social media users construct meaning for the identity of VIs and how this guides their interactions and engagement on social media. Study 1 explores the construction of meaning by analysis of 372 VI posts and 49,866 social media comments using netnography. Study 2 explores the relationship between key concepts (i.e., anthropomorphism, shared reality, digital escapism, and positive affect) using an online survey collected from 209 followers of VIs. This study shows that consumers negotiate the reality of VIs in the comments section with an emphasis on anthropomorphizing. Furthermore, the results of structural equation model suggest that followers’ perceptions of VIs’ moral and cognitive anthropomorphism influences perceived shared reality, which ultimately results in positive affect and social media user engagement
A Novel Continual Learning and Adaptive Sensing State Response‐Based Target Recognition and Long‐Term Tracking Framework for Smart Industrial Applications
No full textPurposeWith the rapid development of artificial intelligence technology, highly intelligent and unmanned factories have become an important trend. In the complex environments of smart factories, the long-term tracking and inspection of specified targets, such as operators and special products, as well as comprehensive visual recognition and decision-making capabilities throughout the whole production process, are critical components of automated unmanned factories. However, challenges such as target occlusion and disappearance frequently occur, complicating long-term tracking. Currently, there is limited research specifically focused on developing robust and comprehensive long-term visual tracking frameworks for unmanned factories, particularly those designed to integrate with embedded platforms and overcome various challenges.MethodsWe first construct three new benchmark datasets in the complex workshop environment of a smart factory (referred to as SF-Complex3 data), which include challenging conditions such as complete occlusion and partial occlusion of targets. A brain memory-inspired approach is used to determine uncertainty estimation parameters, including confidence, peak-to-sidelobe ratio and average peak-to-correlation energy, to develop a continual learning-based adaptive model update method. Additionally, we design a lightweight target detection model to automatically detect and locate targets in the initial frame and during re-detection. Finally, we integrate the algorithm with ground mobile robots and unmanned aerial vehicles-based imaging and processing equipment to build a new visual detection and tracking framework, smart factory complex recognition and tracking.ResultsWe conducted extensive tests on the benchmark UAV20L and SF-Complex3 datasets. The proposed algorithm demonstrates an average performance improvement of 6% when addressing key challenging attributes, compared to state-of-the-art tracking methods. Additionally, the algorithm was capable of running efficiently on embedded platforms, including mobile robots and UAVs, at a real-time speed of 36.4 frames per second.ConclusionsThe proposed SFC-RT framework effectively addresses the challenges of target loss and occlusion in long-term tracking within complex smart factory environments. The framework meets the requirements for real-time performance, robustness and lightweight design, making it well suited for practical deployment
The Good Work Time Series 2025
No full textGood Work builds resilience against socioeconomic and health shocks, and builds resilience for technological transformation. More than any other single factor, access to good jobs will determine prospects for people and places across the country.As evidenced by IFOW’s Pissarides Review into the Future of Work and Wellbeing, technological transformation is having profound impacts on the creation, nature and distribution of good work. The Pissarides Review also found that good quality work mediates better outcomes from AI and automation, and that indicators of good work can serve as measures and proxies for well-functioning local innovation ecosystems and inclusive growth. To understand the progress of these impacts, levels of good work across the regions and nations of Great Britain must be measured.This is what the Good Work Monitor sets out to do. It tracks trends in access to good work across local authorities in England, Scotland and Wales, analysing the most recent available data (until 2024) on six dimensions of good work: employment, economic activity, the share of professional jobs, deroutinisation of occupations, satisfactory working hours, and pay.Due to a lack of data availability, Northern Ireland is not included in the Good Work Monitor at this stage.Reflecting the growing importance of productivity across this Government’s missions, this edition also includes a productivity analysis, using the most current available data.Combined with data from IFOW’s Disruption Index, which tracked technological transformation across England from 2016 – 2022, this unique view over time is designed to help policymakers identify the most effective ways to improve economic, technological, social and health outcomes together, and tailor policy to local challenges.The data in this edition of the Good Work Monitor shows that the trends and trajectories identified in 2023, and flagged again in 2024, are becoming even more entrenched. Differences between top and bottom performers and between local areas are becoming even more pronounced.This 2025 release highlights continuing and significant disparities in labour market outcomes across the nations, regions and local authorities in England, Scotland, and Wales. The particularly pronounced differences in London necessitate a separate analysis of the capital, both in terms of its aggregate position compared to other regions, but also in terms of the depth of the differences which exist between London boroughs.Since our analysis began with data in 2009, aggregate scores have seen a generally positive trend. Recovery after the turbulence of austerity was seen after 2014, with stronger evidence of this in the South of England, and a more volatile picture in the North, in Scotland and in Wales. There was a significant decline in Good Work Total Scores in 2021, the year most impacted by the Covid-19 pandemic. Increased polarisation has characterised subsequent years, with working conditions improving markedly in top-performing regions, while inequalities with low-performing regions have widened.However, contrary to popular narratives, a positive interaction remains between levels of employment and measures of median pay. This should offer reassurance as the Employment Rights Bill progresses through Parliament. Affirmed by new work which has tracked the positive impact of enhanced employment rights on key economic indicators, improvements in pay and conditions can be supported, without the fear that they will lead to fewer jobs.Similarly, the many positive interactions between the six dimensions analysed in this Good Work Monitor should reinforce the case for a new social and economic paradigm of good work. Many components of the Good Work Monitor are still interacting positively, despite a drop in median pay. This offers fresh impetus to the Government’s Industrial Strategy to ensure the creation and protection of good work is a policy priority across different tiers of government, as well as across stages of the technology lifecycle.The findings from IFOW’s Pissarides Review support this and offer a comprehensive and practical set of recommendations for how a new model of human-centred automation can be rolled out. The model it proposes would see a renewal of our regional innovation ecosystem, research and development investment and an embedded process of technology adoption that enhances access to good work.Our research invites a bolder approach to steering responsible innovation; an approach which is nuanced and tailored to local challenges and opportunities, and builds the infrastructure and institutions needed to secure access to and availability of good work across the country.A sharper focus on creating and sustaining good work remains the most effective means of tackling our most pressing challenges and embracing the opportunities of the new technological revolution
Paradox(a): Arts and artists, power, paradox and organized resistance
No full textIn the call for this special issue, we asked: how do the arts, artists and artistic work leverage paradoxes to attract popular attention while also going against the grain of prevailing opinion? How might such work critically reflect on the status quo’s embedded power and uncover new expressive possibilities? Paradox theory has emerged as a significant presence in management and organization studies and as a lens inspiring connection with other theories. Paradox refers to persistent and mutually constituting oppositions inherent to organizing (Clegg et al., 2020; Gaim et al., 2024; Smith and Lewis, 2011) that translate into undecidable trade-offs (Berti and Cunha, 2023). These contradictions may be generative or dysfunctional (Berti et al., 2021; Clegg and Cunha, 2021; Cunha et al., 2022) but can also be sources of novelty and synergy for organizations (Smith and Lewis, 2022). In this special issue, we focus on how paradox theory illuminates the arts as both agents of critique and instruments of engagement. People in key positions in networks of power relations often confront others with absurdly paradoxical demands and situations, forming undecidable trade-off decisions (Berti and Cunha, 2023; Gaim et al., 2021), especially when these others are relatively less advantageously positioned in established circuits of power (Clegg, 2023; Berti and Simpson, 2021; Cunha et al., 2022, 2023; Gaim, Clegg, Cunha and Berti, 2022). Artistic endeavors in multiple media can function not only as a celebration of power (Berger, 1972; Warnke, 1993; Golomstock, 1990) but can also “provide an entry point to problematize existing taken-for-grantedness” (Riaz, 2023, p. 1224). Scoping our invitation in terms of the latter and critiquing the impulse to intellectualize or solve what is emotional and deeply human, we invited authors to submit their work on the arts, artists, power, paradox as a phenomenon and paradox work as a process, focusing on the heuristic potential of paradox as a way of revealing and challenging the status quo, through the many techniques of resistance, such as irony (Badham and Santiago, 2023)
Future productive species for Scotland
No full textThis report describes work led by Forest Research (FR), and commissioned by Scottish Forestry (SF), to support the selection of a shortlist of productive tree species for Scotland. Production of the shortlist delivers a primary action in Scottish Forestry’s Routemap to Resilience
Privacy-enhanced skin disease classification: integrating federated learning in an IoT-enabled edge computing
Get PDFIntroduction: The accurate and timely diagnosis of skin diseases is a critical concern, as many skin diseases exhibit similar symptoms in the early stages. Most existing automated detection/classification approaches that utilize machine learning or deep learning poses privacy issues, as they involve centralized computing and require local storage for data training. Methods: Keeping the privacy of sensitive patient data as a primary objective, in addition to ensuring accuracy and efficiency, this paper presents an algorithm that integrates Federated learning techniques into an IoT-based edge-computing environment. The purpose of the proposed technique is to protect the sensitive data by training the model locally on the edge device and transferring only the weights to the central server where the aggregation takes place. This process ensures data security at the edge level and eliminates the need for centralized storage. Furthermore, the proposed framework enhances the network’s real-time processing capabilities using IoT-integrated sensors, which in turn facilitates swift diagnoses. In addition, this paper also focuses on the design and execution of the federated framework, which includes the processing power, memory, and the number of nodes present in the network. Results: The accuracy and effectiveness of the proposed algorithm are demonstrated using precise parameters, such as accuracy, precision, f1-score, and recall, along with all the intricacies of the secure federated approach. The accuracy achieved by the proposed algorithm is 98.6%. As the model was trained locally, the bandwidth utilization was almost negligible. Discussion: The proposed model can assist skin specialists in diagnosing conditions. Additionally, with federated learning, the model continuously improves as new input data accumulates, enhancing the accuracy of subsequent training rounds
Digital tax administration, investor risk perception, and stock return volatility
Get PDFThis study investigates the impact of digital tax administration on stock return volatility amid heightened market uncertainty. Leveraging the staggered rollout of China's Golden Tax Phase III from 2013 to 2016 as a quasi-natural experiment, we employ a Difference-in-Differences approach on a sample of Chinese listed firms over the period 2010 to 2022. We find that the implementation of digital tax administration significantly alleviates stock return volatility. This stabilizing effect is primarily attributed to reduced investors' perceived risks, which arise from enhanced information transparency, mitigated agency conflicts, and alleviated financial constraints. Drawing upon agency theory and institutional theory, we find that this stabilizing effect is more salient in regions with weaker initial institutional environments (i.e., low tax enforcement efforts, weak legal institutions, and low social trust). Our findings indicate that digital tax administration, as a potent external governance mechanism, can compensate for existing institutional deficiencies. Our study provides crucial insights into the role of digital tax administration in promoting market stability and enhancing corporate governance across diverse regional contexts. Our study is important for both policymakers and firm managers, indicating that leveraging digital tax administration can enhance stock market stability by improving regulatory effectiveness and guiding firms' strategic resource allocation in volatile environments
Enhancing Inclusive Social, Financial, and Health Services for Persons with Disabilities in Saudi Arabia: Insights from Caregivers
Get PDFBackground: Social and financial services are essential for the inclusion and well-being of people with disabilities (PWDs), who often rely on family caregivers to access these systems. In Saudi Arabia, where disability inclusion is a strategic goal under Vision 2030, understanding caregiver experiences is crucial to identifying service gaps and improving accessibility. Objectives: This study aimed to explore caregivers’ perspectives on awareness, perceived barriers, and accessibility of social and financial services for PWDs in Saudi Arabia. The analysis is grounded in Andersen’s Behavioural Model of Health Service Use and the WHO’s International Classification of Functioning, Disability and Health (ICF) framework. Methods: A cross-sectional survey was conducted with 3353 caregivers of PWDs attending specialised day schools. The survey collected data on demographic characteristics, service awareness, utilisation, and perceived obstacles. Exploratory Factor Analysis (EFA) identified latent constructs, and Structural Equation Modelling (SEM) was used to test relationships between awareness, barriers, and accessibility. Results: Findings reveal that over 70% of caregivers lacked awareness of available services, and only about 3% had accessed them. Key challenges included technological barriers, complex procedures, and non-functional or unclear service provider platforms. Both User Barriers and Service Barriers were negatively associated with Awareness and Accessibility. Awareness, in turn, significantly predicted perceived Accessibility. Caregiver demographics, such as age, education, gender, and geographic location, also influenced awareness and service use. Conclusions: There is a pressing need for targeted awareness campaigns, accessible digital service platforms, and simplified service processes tailored to diverse caregiver profiles. Inclusive communication, decentralised outreach, and policy reforms are necessary to enhance service access and promote the societal inclusion of PWDs in alignment with Saudi Arabia’s Vision 2030
DynTex: A real-time generative model of dynamic naturalistic luminance textures
Get PDFThe visual systems of animals work in diverse and constantly changing environments where organism survival requires effective senses. To study the hierarchical brain networks that perform visual information processing, vision scientists require suitable tools, and Motion Clouds (MCs)—a dense mixture of drifting Gabor textons—serve as a versatile solution. Here, we present an open toolbox intended for the bespoke use of MC functions and objects within modeling or experimental psychophysics contexts, including easy integration within Psychtoolbox or PsychoPy environments. The toolbox includes output visualization via a Graphic User Interface. Visualizations of parameter changes in real time give users an intuitive feel for adjustments to texture features like orientation, spatiotemporal frequencies, bandwidth, and speed. Vector calculus tools serve the frame-by-frame autoregressive generation of fully controlled stimuli, and use of the GPU allows this to be done in real time for typical stimulus array sizes. We give illustrative examples of experimental use to highlight the potential with both simple and composite stimuli. The toolbox is developed for, and by, researchers interested in psychophysics, visual neurophysiology, and mathematical and computational models. We argue the case that in all these fields, MCs can bridge the gap between well- parameterized synthetic stimuli like dots or gratings and more complex and less controlled natural videos
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