Visual Decision-Making in Real-Time Business Intelligence: A Social Media Marketing Example

This paper presents a study into the use of visualizations in real-time business intelligence. Different visualization designs for a social media marketing use case are tested and evaluated through the lens of cognitive load theory. By reducing the complexity of visualizations and subsequently cognitive load, end-users can achieve markedly improved decision-making performance in situations where time is critical and data is fast-paced

What is Interaction for Data Visualization?

International audienceInteraction is fundamental to data visualization, but what "interaction" means in the context of visualization is ambiguous and confusing. We argue that this confusion is due to a lack of consensual definition. To tackle this problem, we start by synthesizing an inclusive view of interaction in the visualization community-including insights from information visualization, visual analytics and scientific visualization, as well as the input of both senior and junior visualization researchers. Once this view takes shape, we look at how interaction is defined in the field of human-computer interaction (HCI). By extracting commonalities and differences between the views of interaction in visualization and in HCI, we synthesize a definition of interaction for visualization. Our definition is meant to be a thinking tool and inspire novel and bolder interaction design practices. We hope that by better understanding what interaction in visualization is and what it can be, we will enrich the quality of interaction in visualization systems and empower those who use them

E-Learning

E-learning enables students to pace their studies according to their needs, making learning accessible to (1) people who do not have enough free time for studying - they can program their lessons according to their available schedule; (2) those far from a school (geographical issues), or the ones unable to attend classes due to some physical or medical restriction. Therefore, cultural, geographical and physical obstructions can be removed, making it possible for students to select their path and time for the learning course. Students are then allowed to choose the main objectives they are suitable to fulfill. This book regards E-learning challenges, opening a way to understand and discuss questions related to long-distance and lifelong learning, E-learning for people with special needs and, lastly, presenting case study about the relationship between the quality of interaction and the quality of learning achieved in experiences of E-learning formation

Productivity of incident management with conversational bots-a review

The use of conversational agents (bots) in information systems managed by company’s increases productivity in the development of activities focused on processes such as customer service, healthcare, and presentation. The present work is a systematic literature review that collects articles from 2019 to 2022 in the databases Scopus, Springer, Willey, Indexes-Csic, Taylor & Francis, Pubmed, and Ebsco Host. PRISMA methodology was used to systematize 47 relevant articles. As a result of the analysis, 2/19 very important benefits were obtained, which are: helping to obtain information and facilitating customer service; as for the types of conversational bots, a total of 9 types were found, of which conversational agents and chatbots with artificial intelligence (AI) are the most common; in the case of processes, 3/5 processes that optimize conversational bots were found, where the most prominent are: teaching process, health processes, and customer service processes. An architecture model for conversational bots in incident management is also proposed

Human-machine communication for educational systems design

This book contains the papers presented at the NATO Advanced Study Institute (ASI) on the Basics of man-machine communication for the design of educational systems, held August 16-26, 1993, in Eindhoven, The Netherland

GestureGPT: Zero-shot Interactive Gesture Understanding and Grounding with Large Language Model Agents

Current gesture recognition systems primarily focus on identifying gestures within a predefined set, leaving a gap in connecting these gestures to interactive GUI elements or system functions (e.g., linking a 'thumb-up' gesture to a 'like' button). We introduce GestureGPT, a novel zero-shot gesture understanding and grounding framework leveraging large language models (LLMs). Gesture descriptions are formulated based on hand landmark coordinates from gesture videos and fed into our dual-agent dialogue system. A gesture agent deciphers these descriptions and queries about the interaction context (e.g., interface, history, gaze data), which a context agent organizes and provides. Following iterative exchanges, the gesture agent discerns user intent, grounding it to an interactive function. We validated the gesture description module using public first-view and third-view gesture datasets and tested the whole system in two real-world settings: video streaming and smart home IoT control. The highest zero-shot Top-5 grounding accuracies are 80.11% for video streaming and 90.78% for smart home tasks, showing potential of the new gesture understanding paradigm