Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods. In this study, we present a comprehensive review of methods on neural network based relation...
详细信息
The rise of Artificial Intelligence (AI) has significant implications for local government service delivery, offering considerable advantages alongside inherent risks that warrant careful management. While responsible...
详细信息
The rise of Artificial Intelligence (AI) has significant implications for local government service delivery, offering considerable advantages alongside inherent risks that warrant careful management. While responsible AI has become a focal point in academic and policy discussions, public perceptions remain marginal in these debates. This paper explores how behavioural factors along with perceived risk, local government AI policy awareness and policy expectations influence public intentions to support local government responsible AI practices. Using the Theory of Planned Behaviour, this study examines a multi-factor survey through Confirmatory Factor Analysis, followed by Structural Equation Modelling to assess relationships between key factors. A survey questionnaire, conducted with participants from Australia, the United States, and Spain via the Prolific platform, reveals key insights: (a) perceived risk exhibit a stronger influence than other factors; (b) policy awareness plays a critical role in shaping public intention towards support for responsible practices; (c) greater AI policy awareness correlates with more realistic expectations of local government AI policies; (d) social influence lacks a notable impact in this context. These findings provide valuable guidance for urban policymakers in crafting AI strategies that promote responsible AI implementation within local government services.
This paper proposes a mechanism to accelerate and optimize the energy consumption of a face detection software based on Haar-like cascading classifiers, taking advantage of the features of low-cost asymmetric multicor...
详细信息
The lightweight nature of tensegrity structures calls for the formulation of computational tools that are able to analyze the stability problem of such structures, both in statics and dynamics. The present work analyz...
详细信息
This panel brings together academics and practitioners to discuss the structural problems facing visual effects within the film and television industry today. This discussion is organized around four sub-Topics: 1) la...
详细信息
ISBN:
(纸本)9781450379694
This panel brings together academics and practitioners to discuss the structural problems facing visual effects within the film and television industry today. This discussion is organized around four sub-Topics: 1) labor solutions and what unionization would mean, 2) perceptions of VFX artists and their contributions within the industry by filmmakers and artists themselves, as well as the audience, 3) issues of inclusivity along gender and racial lines, and their effects on work/life balances, and 4) VFX as equal creative partners rather than isolated, temporary, highly-skilled specialists. This panel is a continuation of work begun by the faculty and practitioner organizers, Heath Hanlin (Syracuse University, VPA) and Shaina Holmes (Syracuse University, Newhouse) on studying structural challenges and obstacles to entry for students hoping to enter the VFX industry, in particular women, and artists of color. An earlier iteration of this discussion was held at the 2020 Sundance Film Festival. The hope is that the technical focus and high attendance by industry practitioners at Siggraph, adds practitioner perspectives and educator feedback to the sub-Topics listed above. Simply put, today's film and television industry rely heavily on visual effects, both to advance the story or as a means to hide imperfections to keep the audience engaged without technical distractions. Traditional conceptions of VFX artistry as a production step that comes after primary cinematography is simply outdated. Unfortunately, the economic and work-flow structure of the industry accepts this new reality while at the same time attempting to compensate, credit, and organize VFX artists within the outdated framework. Often this aspect of the conversation is moored in debates surrounding the potentiality of AI to either positively impact the industry (no more underpaid routinized tasks for artists) or negatively (crafts persons and artists can be replaced by algorithms). Rather, a more fruitful
Nowadays, data science projects are usually developed in an unstructured way, which makes it difficult to reproduce. It is also hard to move from an experimental environment to production. Operational workflows such a...
Nowadays, data science projects are usually developed in an unstructured way, which makes it difficult to reproduce. It is also hard to move from an experimental environment to production. Operational workflows such as containerization, continuous deployment, and cloud orchestration allow data science researchers to move a pipeline from a local environment to the cloud. Being aware of the difficulties of setting those workflows up, this paper presents a framework to ease experiment tracking and operationalizing machine learning by combining existent and well-supported technologies. These technologies include Docker, Mlflow, Ray, among others. The framework provides an opinionated workflow to design and execute experiments either on a local environment or the cloud. ml-experiment includes: an automatic tracking system for the most famous machine learning libraries: Tensorflow, Keras, Fastai, Xgboost and Lightgdm, first-class support for distributed training and hyperparameter optimization, and a Command Line Interface (CLI) for packaging and running projects inside containers.
With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex en...
详细信息
Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models ...
详细信息
暂无评论