Developing a robot-guided interactive simon game for physical and cognitive training

Enveloping cognitive or physical rehabilitation into a game highly increases the patients' commitment with their treatment. Specially with children, keeping them motivated is a very time-consuming work, so therapists are demanding tools to help them with this task. NAOTherapist is a generic robotic architecture that uses Automated Planning techniques to autonomously drive noncontact upper-limb rehabilitation sessions for children with a humanoid NAO robot. Our aim is to develop more robotic games for this platform to enrich its variability and possibilities of interaction. The goal of this work is to present our first attempt to develop a different, more complex game that reuses the previous architecture. We contribute with the design description of a novel robotic Simon game that employs upper-limb poses instead of colors and could qualify as a cognitive and physical training. Statistics of evaluation tests with 14 adults and 56 children are displayed and the outcomes are analyzed in terms of human-robot interaction (HRI) quality. The results demonstrate the application-domain generalization capabilities of the NAOTherapist architecture and give an insight to further analyze the therapeutic benefits of the new developed Simon game.This work is partially funded by grant TIN2012-38079-C03-02 and TIN2015-65686- C5-1-R of Spanish Ministerio de Economía y Competitividad. We also want to thank the Joan Miró school of Leganés for their assistance with the evaluations, to the teachers and the management team for their support, and specially to all the children who kindly participated in the evaluation and enjoyed playing with our robots

How do Securities Laws Influence Affect, Happiness, & Trust?

This Article advocates that securities regulators promulgate rules based upon taking into consideration their impacts upon investors\u27 and others\u27 affect, happiness, and trust. Examples of these impacts are consumer optimism, financial stress, anxiety over how thoroughly securities regulators deliberate over proposed rules, investor confidence in securities disclosures, market exuberance, social moods, and subjective well-being. These variables affect and are affected by traditional financial variables, such as consumer debt, expenditures, and wealth; corporate investment; initial public offerings; and securities market demand, liquidity, prices, supply, and volume. This Article proposes that securities regulators can and should evaluate rules based upon measures of affect, happiness, and trust in addition to standard observable financial variables. This Article concludes that the organic statutes of the United States Securities and Exchange Commission are indeterminate despite mandating that federal securities laws consider efficiency among other goals. This Article illustrates analysis of affective impacts of these financial regulatory policies: mandatory securities disclosures; gun-jumping rules for publicly registered offerings; financial education or literacy campaigns; statutory or judicial default rules and menus; and continual reassessment and revision of rules. These regulatory policies impact and are impacted by investors\u27 and other people\u27s affect, happiness, and trust. Thus, securities regulators can and should evaluate such affective impacts to design effective legal policy

Democratic Transitions and the Future of Asylum Law

The United States\u27s commitment to protecting refugees is dying a slow death. Two developments have contributed to its demise. The first, widely heralded, is the United States Congress\u27s evisceration of procedural safeguards such as judicial review. The second development is more insidious: expansion of the asylum law doctrine, which holds that changed country conditions can defeat an otherwise valid asylum claim. In an age in which democracy seems triumphant throughout the world, the combination of severely curtailed judicial review and mechanical application of the changed conditions doctrine relegates refugees, as well as asylum law itself, to an uncertain future.\u27 This article argues that the rise of the changed country conditions doctrine stems from judicial and administrative confusion about both the role of both subjective and objective factors in asylum law and the nature of democratic transitions

Multimodal Sentiment Analysis: Perceived vs Induced Sentiments

Social media has created a global network where people can easily access and exchange vast information. This information gives rise to a variety of opinions, reflecting both positive and negative viewpoints. GIFs stand out as a multimedia format offering a visually engaging way for users to communicate. In this research, we propose a multimodal framework that integrates visual and textual features to predict the GIF sentiment. It also incorporates attributes including face emotion detection and OCR generated captions to capture the semantic aspects of the GIF. The developed classifier achieves an accuracy of 82.7% on Twitter GIFs, which is an improvement over state-of-the-art models. Moreover, we have based our research on the ReactionGIF dataset, analysing the variance in sentiment perceived by the author and sentiment induced in the reade

Latent variable pictorial structure for human pose estimation on depth images

Prior models of human pose play a key role in state-of-the-art techniques for monocular pose estimation. However, a simple Gaussian model cannot represent well the prior knowledge of the pose diversity on depth images. In this paper, we develop a latent variable-based prior model by introducing a latent variable into the general pictorial structure. Two key characteristics of our model (we call Latent Variable Pictorial Structure) are as follows: (1) it adaptively adopts prior pose models based on the estimated value of the latent variable; and (2) it enables the learning of a more accurate part classifier. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods in recognition rate on the public datasets

Automated Face Recognition: Challenges and Solutions

Automated face recognition (AFR) aims to identify people in images or videos using pattern recognition techniques. Automated face recognition is widely used in applications ranging from social media to advanced authentication systems. Whilst techniques for face recognition are well established, the automatic recognition of faces captured by digital cameras in unconstrained, real‐world environment is still very challenging, since it involves important variations in both acquisition conditions as well as in facial expressions and in pose changes. Thus, this chapter introduces the topic of computer automated face recognition in light of the main challenges in that research field and the developed solutions and applications based on image processing and artificial intelligence methods