Tootfinder

This https://arxiv.org/abs/2401.05871 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Enhancing Personality Recognition in Dialogue by Data Augmentation and Heterogeneous Conversational Graph Networks
Personality recognition is useful for enhancing robots' ability to tailor user-adaptive responses, thus fostering rich human-robot interactions. One of the challenges in this task is a limited number of speakers in existing dialogue corpora, which hampers the development of robust, speaker-independent personality recognition models. Additionally, accurately modeling both the interdependencies among interlocutors and the intra-dependencies within the speaker in dialogues remains a significant is…

A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN
Cristiana Tiago, Andrew Gilbert, Ahmed S. Beela, Svein Arne Aase, Sten Roar Snare, Jurica Sprem
https://arxiv.org/abs/2403.05384

A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN
Due to privacy issues and limited amount of publicly available labeled datasets in the domain of medical imaging, we propose an image generation pipeline to synthesize 3D echocardiographic images with corresponding ground truth labels, to alleviate the need for data collection and for laborious and error-prone human labeling of images for subsequent Deep Learning (DL) tasks. The proposed method utilizes detailed anatomical segmentations of the heart as ground truth label sources. This initial d…

A Framework for Leveraging Human Computation Gaming to Enhance Knowledge Graphs for Accuracy Critical Generative AI Applications
Steph Buongiorno, Corey Clark
https://arxiv.org/abs/2404.19729 https://arxiv.org/pdf/2404.19729
arXiv:2404.19729v1 Announce Type: new
Abstract: External knowledge graphs (KGs) can be used to augment large language models (LLMs), while simultaneously providing an explainable knowledge base of facts that can be inspected by a human. This approach may be particularly valuable in domains where explainability is critical, like human trafficking data analysis. However, creating KGs can pose challenges. KGs parsed from documents may comprise explicit connections (those directly stated by a document) but miss implicit connections (those obvious to a human although not directly stated). To address these challenges, this preliminary research introduces the GAME-KG framework, standing for "Gaming for Augmenting Metadata and Enhancing Knowledge Graphs." GAME-KG is a federated approach to modifying explicit as well as implicit connections in KGs by using crowdsourced feedback collected through video games. GAME-KG is shown through two demonstrations: a Unity test scenario from Dark Shadows, a video game that collects feedback on KGs parsed from US Department of Justice (DOJ) Press Releases on human trafficking, and a following experiment where OpenAI's GPT-4 is prompted to answer questions based on a modified and unmodified KG. Initial results suggest that GAME-KG can be an effective framework for enhancing KGs, while simultaneously providing an explainable set of structured facts verified by humans.

This https://arxiv.org/abs/2312.11843 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Enhancing Social Decision-Making of Autonomous Vehicles: A Mixed-Strategy Game Approach With Interaction Orientation Identification
The integration of Autonomous Vehicles (AVs) into existing human-driven traffic systems poses considerable challenges, especially within environments where human and machine interactions are frequent and complex, such as at unsignalized intersections. To deal with these challenges, we introduce a novel framework predicated on dynamic and socially-aware decision-making game theory to augment the social decision-making prowess of AVs in mixed driving environments. This comprehensive framework is …

High-skilled Human Workers in Non-Routine Jobs are Susceptible to AI Automation but Wage Benefits Differ between Occupations
Pelin Ozgul, Marie-Christine Fregin, Michael Stops, Simon Janssen, Mark Levels
https://arxiv.org/abs/2404.06472

High-skilled Human Workers in Non-Routine Jobs are Susceptible to AI Automation but Wage Benefits Differ between Occupations
Artificial Intelligence (AI) will change human work by taking over specific job tasks, but there is a debate which tasks are susceptible to automation, and whether AI will augment or replace workers and affect wages. By combining data on job tasks with a measure of AI susceptibility, we show that more highly skilled workers are more susceptible to AI automation, and that analytical non-routine tasks are at risk to be impacted by AI. Moreover, we observe that wage growth premiums for the lowest …

My latest blog.
Why have people called this "brilliant," "gorgeous," "beautiful"?
And said "Your posts are always good. But this one was especially good."?
I guess you'll just have to find out for yourself!

"Sustainability"? Nah. What About a Business Narrative That Centers the Living World?
Augmented Intelligence stories from Winston, Friend…and a friend! Three narratives of human and business futures, with different seeds at the center.

This https://arxiv.org/abs/2312.11843 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Enhancing Social Decision-Making of Autonomous Vehicles: A Mixed-Strategy Game Approach With Interaction Orientation Identification
The integration of Autonomous Vehicles (AVs) into existing human-driven traffic systems poses considerable challenges, especially within environments where human and machine interactions are frequent and complex, such as at unsignalized intersections. To deal with these challenges, we introduce a novel framework predicated on dynamic and socially-aware decision-making game theory to augment the social decision-making prowess of AVs in mixed driving environments. This comprehensive framework is …

This https://arxiv.org/abs/2308.02151 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
Recent months have seen the emergence of a powerful new trend in which large language models (LLMs) are augmented to become autonomous language agents capable of performing objective oriented multi-step tasks on their own, rather than merely responding to queries from human users. Most existing language agents, however, are not optimized using environment-specific rewards. Although some agents enable iterative refinement through verbal feedback, they do not reason and plan in ways that are comp…

This https://arxiv.org/abs/2403.11384 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

A Systematic Review of XR-based Remote Human-Robot Interaction Systems
This survey provides an exhaustive review of the applications of extended reality (XR) technologies in the field of remote human-computer interaction (HRI). We developed a systematic search strategy based on the PRISMA methodology. From the initial 2,561 articles selected, 100 research papers that met our inclusion criteria were included. We categorized and summarized the domain in detail, delving into XR technologies, including augmented reality (AR), virtual reality (VR), and mixed reality (M…

This https://arxiv.org/abs/2402.16063 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Citation-Enhanced Generation for LLM-based Chatbot
Large language models (LLMs) exhibit powerful general intelligence across diverse scenarios, including their integration into chatbots. However, a vital challenge of LLM-based chatbots is that they may produce hallucinated content in responses, which significantly limits their applicability. Various efforts have been made to alleviate hallucination, such as retrieval augmented generation and reinforcement learning with human feedback, but most of them require additional training and data annota…