Dynamic Probabilistic Model Checking for Sensor Validation in Industry 4.0 Applications

Industry 4.0 adopts Internet of Things (IoT) and service-oriented architectures to integrate Cyber-Physical Systems and Enterprise Planning into manufacturing operations. This kind of integration consists of a combination of connected sensors and run-time control algorithms. Consequential control decisions are driven by sensor-generated data. Hence, the trustworthiness of the sensor network readings is increasingly crucial to guarantee the performance and the quality of a manufacturing task. However, existing methodologies to test such systems often do not scale to the complexity and dynamic nature of today’s sensor networks. This paper proposes a novel run-time verification framework combining sensor-level fault detection and system-level probabilistic model checking. This framework can rigorously quantify the trustworthiness of sensor readings, hence enabling formal reasoning for system failure prediction. We evaluated our approach on an industrial turn-mill machine equipped with a sensor network to monitor its main components continuously. The results indicate that the proposed verification framework involving the quantified sensor’s trustworthiness enhances the accuracy of the system failure prediction

Pedagogical Agents for Fostering Question-Asking Skills in Children

Question asking is an important tool for constructing academic knowledge, and a self-reinforcing driver of curiosity. However, research has found that question asking is infrequent in the classroom and children's questions are often superficial, lacking deep reasoning. In this work, we developed a pedagogical agent that encourages children to ask divergent-thinking questions, a more complex form of questions that is associated with curiosity. We conducted a study with 95 fifth grade students, who interacted with an agent that encourages either convergent-thinking or divergent-thinking questions. Results showed that both interventions increased the number of divergent-thinking questions and the fluency of question asking, while they did not significantly alter children's perception of curiosity despite their high intrinsic motivation scores. In addition, children's curiosity trait has a mediating effect on question asking under the divergent-thinking agent, suggesting that question-asking interventions must be personalized to each student based on their tendency to be curious.Comment: Accepted at CHI 202

Children s Acceptance of a Collaborative Problem Solving Game Based on Physical Versus Digital Learning Spaces

[EN] Collaborative problem solving (CPS) is an essential soft skill that should be fostered from a young age. Research shows that a good way of teaching such skills is through video games; however, the success and viability of this method may be affected by the technological platform used. In this work we propose a gameful approach to train CPS skills in the form of the CPSbot framework and describe a study involving 80 primary school children on user experience and acceptance of a game, Quizbot, using three different technological platforms: two purely digital (tabletop and handheld tablets) and another based on tangible interfaces and physical spaces. The results show that physical spaces proved to be more effective than the screen-based platforms in several ways, as well as being considered more fun and easier to use by the children. Finally, we propose a set of design considerations for future gameful CPS systems based on the observations made during this study.Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (project TIN2014-60077-R); Spanish Ministry of Education, Culture and Sport (with fellowship FPU14/00136) and Conselleria d'Educacio, Cultura i Esport (Generalitat Valenciana, Spain) (grant ACIF/2014/214).Jurdi, S.; García Sanjuan, F.; Nácher-Soler, VE.; Jaén Martínez, FJ. (2018). Children s Acceptance of a Collaborative Problem Solving Game Based on Physical Versus Digital Learning Spaces. Interacting with Computers. 30(3):187-206. https://doi.org/10.1093/iwc/iwy006S18720630

Gesture Recognition Performance Score: A New Metric to Evaluate Gesture Recognition Systems

Abstract. In spite of many choices available for gesture recognition algorithms, the selection of a proper algorithm for a specific application remains a difficult task. The available algorithms have different strengths and weaknesses making the matching between algorithms and applications complex. Accurate evaluation of the performance of a gesture recognition algorithm is a cumbersome task. Performance evaluation by recognition accuracy alone is not sufficient to predict its successful realworld implementation. We developed a novel Gesture Recognition Performance Score (GRP S) for ranking gesture recognition algorithms, and to predict the success of these algorithms in real-world scenarios. The GRP S is calculated by considering different attributes of the algorithm, the evaluation methodology adopted, and the quality of dataset used for testing. The GRP S calculation is illustrated and applied on a set of vision based hand/ arm gesture recognition algorithms reported in the last 15 years. Based on GRP S a ranking of hand gesture recognition algorithms is provided. The paper also presents an evaluation metric namely Gesture Dataset Score (GDS) to quantify the quality of gesture databases. The GRP S calculator and results are made publicly available (http://software.ihpc.a-star.edu.sg/grps/)

Iterative design process for robots with personality

Previous research has shown that autonomous robots tend to induce the perception of a personality through their behavior and appearance. It has therefore been suggested that the personality of a robot can be used as a design guideline. A welldefined and clearly communicated personality can serve as a mental model of the robot and facilitate the interaction. From design perspective, this raises the question what kind of personality to design for a robot and how to express this personality? In this paper, we describe a process to design and evaluate personality and expressions for products. We applied this to design the personality and expressions in the behavior of a domestic robot

Towards a Design Method for Expressive Robots

Autonomous robots tend to induce the perception of a personality through their behavior and appearance. It has been suggested that the personality of a robot can be used as a design guideline and as a mental model of the robot. We propose a method to design and evaluate personality and expressions for domestic robots

EAD-GAN: a generative adversarial network for disentangling affine transforms in images

This article proposes a generative adversarial network called explicit affine disentangled generative adversarial network (EAD-GAN), which explicitly disentangles affine transform in a self-supervised manner. We propose an affine transform regularizer to force the InfoGAN to have explicit properties of affine transform. To facilitate training an affine transform encoder, we decompose the affine matrix into two separate matrices and infer the explicit transform parameters by the least-squares method. Unlike the existing approaches, representations learned by the proposed EAD-GAN have clear physical meaning, where transforms, such as rotation, horizontal and vertical zooms, skews, and translations, are explicitly learned from training data. Thus, we set different values of each transform parameter individually to generate specifically affine transformed data by the learned network. We show that the proposed EAD-GAN successfully disentangles these attributes on the MNIST, CelebA, and dSprites datasets. EAD-GAN achieves higher disentanglement scores with a large margin compared to the state-of-the-art methods on the dSprites dataset. For example, on the dSprites dataset, EAD-GAN achieves the MIG and DCI score of 0.59 and 0.96 respectively, compared to 0.37 and 0.71, respectively, for the state-of-the-art methods.Economic Development Board (EDB)Published versionThis work was supported in part by the Singapore Economic Development Board Industrial Postgraduate Program under Grant S17-1298-IPP-II