27 papers · ranked by Valyu relevance
Borja Requena, Giovanni Cassani, Jacopo Tagliabue, Ciro Greco + 1 more
'Lucas Lacasa'] We address the problem of user intent prediction from clickstream data of an e-commerce website via two conceptually different approaches: a hand-crafted feature-based classification and a deep learning-based classification. In both approaches, we deliberately coarse-grain a new clickstream proprietary…
Trisha Mittal, Sanjoy Chowdhury, Pooja Guhan, Snikitha Chelluri + 1 more
'Dinesh Manocha'] Increasing use of social media has resulted in many detrimental effects in youth. With very little control over multimodal content consumed on these platforms and the false narratives conveyed by these multimodal social media postings, such platforms often impact the mental well-being of the users. To…
Chen Qu, Yang Liu, W. Bruce Croft, Yongfeng Zhang + 2 more
'Johanne R. Trippas' 'Minghui Qiu'] Conversational assistants are being progressively adopted by the general population. However, they are not capable of handling complicated information-seeking tasks that involve multiple turns of information exchange. Due to the limited communication bandwidth in conversational…
Chien-Ming Huang, Sean Andrist, Allison Sauppé, Bilge Mutlu
In everyday interactions, humans naturally exhibit behavioral cues, such as gaze and head movements, that signal their intentions while interpreting the behavioral cues of others to predict their intentions. Such intention prediction enables each partner to adapt their behaviors to the intent of others, serving a…
Lei Shi, Paul‐Christian Bürkner, Andreas Bulling
Inferring human intentions is a core challenge in human-AI collaboration but while Bayesian methods struggle with complex visual input, deep neural network (DNN) based methods do not provide uncertainty quantifications. In this work we combine both approaches for the first time and show that the predicted next action…
Yuanyuan Han, Juanjuan Zhai, Ping Li, Rongbin Yang
To address the low efficiency of feature mining and limited prediction accuracy in enterprise service user intent prediction, a research proposes an enterprise service user intention prediction model that integrates heuristic variants inspired by Kmeans++and Stacking ensemble learning. The model improves traditional…
Joseph Russell, Jeroen H. M. Bergmann, Vikranth H. Nagaraja, Jan Vanus + 3 more
Intent sensing-the ability to sense what a user wants to happen-has many potential technological applications. Assistive medical devices, such as prosthetic limbs, could benefit from intent-based control systems, allowing for faster and more intuitive control. The accuracy of intent sensing could be improved by using…
L. Ceravolo, S. Schaerlaeken, S. Frühholz, D. Glowinski + 1 more
Integrating and predicting intentions and actions of others are crucial components of social interactions, but the behavioral and neural underpinnings of such mechanisms in altered perceptual conditions remain poorly understood. We demonstrated that expertise was necessary to successfully understand and evaluate…
Kevin Ros, Dhruv Pandya, ChengXiang Zhai
The ability to predict a user's information need would have wideranging implications, from saving time and effort to mitigating vocabulary gaps. We study how to interactively predict a user's information need by letting them select a pre-search context (e.g., a paragraph, sentence, or singe word) and specify an…
Jingwei Zhao, Yuhua Wen, Qifei Li, Minchi Hu + 7 more
Intent recognition aims to identify users' underlying intentions, traditionally focusing on text in natural language processing. With growing demands for natural human-computer interaction, the field has evolved through deep learning and multimodal approaches, incorporating data from audio, vision, and physiological…
Esther Ulitzsch, Vincent Ulitzsch, Qiwei He, Oliver Lüdtke
Early detection of risk of failure on interactive tasks comes with great potential for better understanding how examinees differ in their initial behavior as well as for adaptively tailoring interactive tasks to examinees’ competence levels. Drawing on procedures originating in shopper intent prediction on e-commerce…
Riccardo De Benedictis, Alessandro Umbrico, Francesca Fracasso, Gabriella Cortellessa + 2 more
'Gabriella Cortellessa' 'Andrea Orlandini' 'Amedeo Cesta'] Socially assistive robotics (SAR) aims at designing robots capable of guaranteeing social interaction to human users in a variety of assistance scenarios that range, e.g., from giving reminders for medications to monitoring of Activity of Daily Living, from…
Wei Peng, Yue Hu, Luxi Xing, Yuqiang Xie + 2 more
'Yajing Sun'] Intention, emotion and action are important elements in human activities. Modeling the interaction process between individuals by analyzing the relationships between these elements is a challenging task. However, previous work mainly focused on modeling intention and emotion independently, and neglected…
Sevinj Yolchuyeva, Géza Németh, Bálint Gyires-Tóth
Self-attention networks (SAN) have shown promising performance in various Natural Language Processing (NLP) scenarios, especially in machine translation. One of the main points of SANs is the strength of capturing long-range and multi-scale dependencies from the data. In this paper, we present a novel intent detection…
June-Woo Kim, Hyekyung Yoon, Ho-Young Jung, Christoph M. Friedrich
Successful applications of deep learning technologies in the natural language processing domain have improved text-based intent classifications. However, in practical spoken dialogue applications, the users’ articulation styles and background noises cause automatic speech recognition (ASR) errors, and these may lead…
Eliana Vassena, James Deraeve, William H. Alexander
Human behavior is strongly driven by the pursuit of rewards. In daily life, however, benefits mostly come at a cost, often requiring that effort be exerted in order to obtain potential benefits. Medial prefrontal cortex (MPFC) and dorsolateral prefrontal cortex (DLPFC) are frequently implicated in the expectation of…
Michelle L. Eisenberg, Jeffrey M. Zacks, Shaney Flores
The ability to predict what is going to happen in the near future is integral for daily functioning. Previous research suggests that predictability varies over time, with increases in prediction error at those moments that people perceive as boundaries between meaningful events. These moments also tend to be points of…
Yupei Chen, Zhibo Yang, Seoyoung Ahn, Dimitris Samaras + 2 more
Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control—saliency. We introduce COCO-Search18, the first dataset of laboratory-quality goal-directed behavior…
Eghbal A. Hosseini, Evelina Fedorenko
Predicting upcoming events is critical to our ability to effectively interact with our environment and conspecifics. In natural language processing, transformer models, which are trained on next-word prediction, appear to construct a general-purpose representation of language that can support diverse downstream tasks.…
M. I. Garrido, E. G. Rowe, Veronika Halász, J. B. Mattingley
Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we…
Juliane Schubert, Nina Suess, Nathan Weisz
Predictive processing theories, which model the brain as a “prediction machine”, explain a wide range of cognitive functions, including learning, perception and action. Furthermore, it is increasingly accepted that aberrant prediction tendencies play a crucial role in psychiatric disorders. Given this explanatory value…
Long Qian, Xin Lu, Parvez Haris, Jianyong Zhu + 2 more
Clinical trials are crucial for drug development, but they require significant time and financial resources. Additionally, uncertainties may arise during these trials concerning their results due to concerns surrounding effectiveness, safety, or the enrollment of participants. If robust AI (artificial intelligence)…
Authors not listed
The prediction of organic chemical reactions has historically presented significant challenges owing to the inherent complexity and mechanistic diversity of reaction processes. In this work, we present LoRA-Chem, an innovative modular framework that demonstrates remarkable performance in predicting individual organic…
William Borrelli, Joshua Schrier
Forward and retrosynthetic organic reaction prediction are challenging applications of artificial intelligence (AI) research in chemistry. IBM’s freely available RXN for Chemistry (https://rxn.res.ibm.com) treats reaction prediction as a translation problem, by using transformer-based machine learning models trained on…
Amol Thakkar, Nidhal Selmi, Jean-Louis Reymond, Ola Engkvist + 1 more
Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. 'Ring Breaker' enables…
Kangjie Lin, Junren Li, Haoyu Lin, Jianfeng Pei + 1 more
Reaction selectivity and yield prediction are important for chemical synthesis. Most existing computational methods use either computational expensive and complicated quantum mechanics-based models that are not easy for experimental chemists to use or black-box deep learning models that do not generalize well outside…
Adarsh Arun, Zhen Guo, Simon Sung, Alexei Lapkin
Automated prediction of reaction impurities can be useful in facilitating rapid early-stage reaction development, synthesis planning and optimization. Existing reaction predictors are catered towards main product prediction, and are often black-box, making it difficult to troubleshoot erroneous outcomes. This work…