5,971 research outputs found
Interaction Visual Transformer for Egocentric Action Anticipation
Human-object interaction is one of the most important visual cues that has
not been explored for egocentric action anticipation. We propose a novel
Transformer variant to model interactions by computing the change in the
appearance of objects and human hands due to the execution of the actions and
use those changes to refine the video representation. Specifically, we model
interactions between hands and objects using Spatial Cross-Attention (SCA) and
further infuse contextual information using Trajectory Cross-Attention to
obtain environment-refined interaction tokens. Using these tokens, we construct
an interaction-centric video representation for action anticipation. We term
our model InAViT which achieves state-of-the-art action anticipation
performance on large-scale egocentric datasets EPICKTICHENS100 (EK100) and
EGTEA Gaze+. InAViT outperforms other visual transformer-based methods
including object-centric video representation. On the EK100 evaluation server,
InAViT is the top-performing method on the public leaderboard (at the time of
submission) where it outperforms the second-best model by 3.3% on mean-top5
recall
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People-centric Emission Reduction in Buildings: A Data-driven and Network Topology-based Investigation
There is a growing consensus among policymakers that we need a human-centric low-carbon transition. There are few studies on how to do it effectively, especially in the context of emissions reduction in the building sector. It is critical to investigate public sentiment and attitudes towards this aspect of climate action, as the building and construction sector accounts for 40% of global carbon emissions. Our methodology involves a multi-method approach, using a data-driven exploration of public sentiment using 256,717 tweets containing #emission and #building between 2009 - 2021. Using graph theory-led metrics, a network topology-based investigation of hashtag co-occurrences was used to extract highly influential hashtags. Our results show that public sentiment is reactive to global climate policy events. Between 2009-2012, #greenbuilding, #emissions were highly influential, shaping the public discourse towards climate action. In 2013-2016, #lowcarbon, #construction and #energyefficiency had high centrality scores, which were replaced by hashtags like #climatetec, #netzero, #climateaction, #circulareconomy, and #masstimber, #climatejustice in 2017-2021. Results suggest that the current building emission reduction context emphasises the social and environmental justice dimensions, which is pivotal to an effective people-centric policymaking
Behavioral Intention Prediction in Driving Scenes: A Survey
In the driving scene, the road agents usually conduct frequent interactions
and intention understanding of the surroundings. Ego-agent (each road agent
itself) predicts what behavior will be engaged by other road users all the time
and expects a shared and consistent understanding for safe movement. Behavioral
Intention Prediction (BIP) simulates such a human consideration process and
fulfills the early prediction of specific behaviors. Similar to other
prediction tasks, such as trajectory prediction, data-driven deep learning
methods have taken the primary pipeline in research. The rapid development of
BIP inevitably leads to new issues and challenges. To catalyze future research,
this work provides a comprehensive review of BIP from the available datasets,
key factors and challenges, pedestrian-centric and vehicle-centric BIP
approaches, and BIP-aware applications. Based on the investigation, data-driven
deep learning approaches have become the primary pipelines. The behavioral
intention types are still monotonous in most current datasets and methods
(e.g., Crossing (C) and Not Crossing (NC) for pedestrians and Lane Changing
(LC) for vehicles) in this field. In addition, for the safe-critical scenarios
(e.g., near-crashing situations), current research is limited. Through this
investigation, we identify open issues in behavioral intention prediction and
suggest possible insights for future research.Comment: 254 reference
Probes and Sensors: The Design of Feedback Loops for Usability Improvements
The importance of user-centric design methods in the design of programming tools is now well accepted. These methods depend on creating a feedback loop between the designers and their users, providing data about developers, their needs and behaviour gathered through various means. These include controlled experiments, field observations, as well as analytical frameworks. However, whilst there have been a number of experiments detailed, quantitative data is rarely used as part of the design process. Part of the reason for this might be that such feedback loops are hard to design and use in practice. Still, we believe there is potential in this approach and opportunities in gathering this kind of ‘big data’. In this paper, we sketch a framework for reasoning about these feedback loops - when data gathering may make sense and for how to incorporate the results of such data gathering into the programming tool design process. We illustrate how to use the framework on two case studies and outline some of the challenges in instrumentation and in knowing when and how to act on signals
Mission-Centric Learning: Developing Students’ Workplace Readiness Skills
We discuss and evaluate the implementation of a mission-centric course project that is strategically tied to learning outcomes important to colleges of business. Specifically, to support our college’s mission, undergraduate students enrolled in a training and development class were tasked with applying course concepts to assess the need for, to design, and to deliver (to other business students) workplace readiness training. To aid other management educators interested in adopting similar strategically aligned and feedback-rich learning experiences, we outline and discuss relevant project planning, design, and facilitation issues, as well as present a summary of initial results derived from this project
Using big data for customer centric marketing
This chapter deliberates on “big data” and provides a short overview of business intelligence and emerging analytics. It underlines the importance of data for customer-centricity in marketing. This contribution contends that businesses ought to engage in marketing automation tools and apply them to create relevant, targeted customer experiences. Today’s business increasingly rely on digital media and mobile technologies as on-demand, real-time marketing has become more personalised than ever. Therefore, companies and brands are striving to nurture fruitful and long lasting relationships with customers. In a nutshell, this chapter explains why companies should recognise the value of data analysis and mobile applications as tools that drive consumer insights and engagement. It suggests that a strategic approach to big data could drive consumer preferences and may also help to improve the organisational performance.peer-reviewe
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