To improve communication and collaboration in virtual reality (VR), we suggest going beyond improving behavioral realism on participant avatars. Leveraging VR capabilities, our approach focuses on transforming and amplifying social interactions. VR technology prevents users from naturally expressing social signals, accurately detecting and transmitting them due to still limited technological capabilities, or even fully perceiving them. We propose an augmented model aimed at enhancing collaboration by displaying non-verbal engagement behaviors on listeners’ avatars, even if not executed. In a trio VR session organizing a cultural event, participants were represented by avatars, with or without the augmented model in a within-subject design. Results indicate significantly improved social presence and significant changes in participants’ behaviors, particularly their gaze, demonstrating the transformative potential of social augmentations for collaborative tasks in VR.

EXplainable AI (XAI) offers a wide range of algorithmic solutions to the problem of AI's opacity, but ensuring of their usefulness remains a challenge. In this study, we propose an multi-explanation XAI system using surrogate rules, LIME and nearest neighbor on a random forest. Through an experiment in an e-sports prediction task, we demonstrate the feasibility and measure the usefulness of working with multiple forms of explanation. Considering users' preferences, we offer new perspectives for XAI design and evaluation, highlighting the concept of data difficulty and of the idea of prior agreement between users and AI.

This paper investigates the impact of displaying the facial expressions of a user in real time on the performance of the task, the social presence and the behavioral changes of the users interacting in a Virtual Reality environment. To evaluate this approach, we conducted a study where the users collaborated to build a TV stand in dyad including a novice and an expert assistant. The collaborative task was divided into two independent phases: a face-to-face discussion phase without object manipulation, and a furniture assembly phase with object manipulation. Our results indicate that the proposed approach can increase social presence and lead to gaze behavior changes in multi-user environments during the face-to-face phase.

Reading in public spaces can often be tricky if we wish to keep the contents away from the prying eye. We propose Private Reader, an eye-tracking approach towards maintaining privacy while reading by rendering only the portion of text that is gazed by the reader. We conducted a user study by evaluating for both the reader and observer in terms of privacy, reading comfort, and reading speed for three reading modes; normal, underscored, and scrambled text. "Scrambled" performs best in terms of perceived effort and frustration for the shoulder surfer. Our contribution is threefold; we developed a system to preserve privacy by rendering only the text at gaze-point of the reader, we conducted a user study to evaluate user preferences and subjective task load, and we suggested several scenarios where Private Reader is useful in public spaces.

We present the Vocabulometer, a reading assistant system designed to record the reading activity of a user with an eye tracker and to extract mutual information about the users and the read documents. The Vocabulometer stands as a web platform and can be used for analyzing the comprehension of the user, the comprehensibility of the document, predicting the difficult words, recommending document according to the reader's in order to increase his skills, etc. Since the last years, with the development of low-cost eye trackers, the technology is now accessible for many people, which will allow using data mining and machine learning algorithms for the mutual analysis of documents and readers.

Comics is usually classified into broad categories called "genres" according to its contents such as comedy, horror, science fiction, etc. Because a genre expresses a comics story briefly, people read comics which has contents based on their interest, by relying on comics genres. However, giving only one genre to one comic cannot express the detailed difference of the story. In this paper, we propose a system for generating comics story representation as a sub-sequence of genres. Our comics story representation can be applied to a new search engine based on stories or to a recommendation system which analyzes the tastes of the user's favorite comics by finding comics with similar story representation. We use a deep neural network to classify each page into the corresponding genre. Experimental results confirm the advantage of the proposed system.

This paper presents an approach for identifying reader specific difficult words while someone is reading a textual document. The work is motivated by the need of developing human-document interaction systems, in general and creating person-specific online educational content, in particular. Eye gaze information gives person specific behavior whereas textual content is analyzed to get general linguistic aspect of the document content. These two pieces of information are fused together through machine learning algorithms to identify the set of difficult words for a particular reader reading a particular document. An annotated dataset has been created where each word in a document is marked with its bounding box information and each reader identifies a set of difficult words while reading the document. The dataset consists of sixteen documents and each document is read by five subjects. The method is evaluated through recall-precision analysis. The impressive precision at high recall attests the feasibility of building a practical application based on this research. The experiment further brings out several interesting facts about human reading behaviour.

Estimating automatically the degree of language skill by analyzing the eye movements is a promising way to help people from all over the world to learn a new language. In this study, we focus on the English skills of non-native speakers. Our aim is to provide an algorithm that can assess accurately and automatically the TOEIC score after reading English texts for few minutes. As a first step towards this direction, we propose an algorithm that can predict accurately this score after reading and answering some questions about the comprehension of few English texts. We use an eye tracker in order to record the eye gaze, i.e. the positions where the reader is looking at. Then we extract several features to characterize the behavior, and consequently the skill of the reader. We also add a feature based on the number of correct answers to the questions. By using a machine learning based on multivariate regression, the score is estimated user independently. A backward stepwise feature selection is used to select the relevant features and to optimize the estimation. As a main result, the TOEIC score is estimated with 21.7 points of mean absolute error for 21 subjects after reading and answering the questions of only 3 documents.

Instead of analyzing directly the document images, analyzing the document reading can offer new perspectives for extracting information about both the reader and the document. Analyzing how people read texts can help to understand the cognitive process of the reading and might lead to new approaches and new solutions for pattern recognition and document image analysis. It can also lead to create smart documents that can measure reading information, provide feedback and adapt themselves depending on the behavior of the readers. As a step towards document reading analysis, the authors propose in this paper a solution for extracting the reading information and creating a “reading-life log”. This reading-life log contains basic features that can be used for many different kinds of applications. A tag cloud evolving according to the reading is presented as a first application of the reading-life log.

The main contribution of this paper is a new method for classifying document images by combining textual features extracted with the Bag of Words (BoW) technique and visual features extracted with the Bag of Visual Words (BoVW) technique. The BoVW is widely used within the computer vision community for scene classification or object recognition but few applications for the classification of entire document images have been submitted. While previous attempts have been showing disappointing results by combining visual and textual features with the Borda-count technique, we're proposing here a combination through learning approach. Experiments conducted on a 1925 document image industrial database reveal that this fusion scheme significantly improves the classification performances. Our concluding contribution deals with the choosing and tuning of the BoW and/or BoVW techniques in an industrial context.

This article presents a new method to index document images. This work is done in an industrial context where thousands of document images are daily digitized, these images have to be sorted in different classes like payroll, various bills, information letters. We propose a software method which aims to accelerate this task. Usually, the number of document classes is a priori unknown. In this paper, we propose an automatic estimation of this class number. According to this class number, we use a clustering algorithm in order to group document images. After this step, we propose an assisted classification tool based on content based image retrieval method (CBIR). For each cluster, a reference image is automatically selected then considering a similarity measure, the other images are sorted and shown to the user. By interacting with the process, the user can reject wrong images. The user feedback is automatically taken into account to enhance the similarity measure by weighting each feature. The first tests show that, on average, databases are indexed 3 times faster with our assisted classification method than with a standard manual classification process.