A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed

A Satisfiability Modulo Theory Approach to Secure State Reconstruction in Differentially Flat Systems Under Sensor Attacks

We address the problem of estimating the state of a differentially flat system from measurements that may be corrupted by an adversarial attack. In cyber-physical systems, malicious attacks can directly compromise the system's sensors or manipulate the communication between sensors and controllers. We consider attacks that only corrupt a subset of sensor measurements. We show that the possibility of reconstructing the state under such attacks is characterized by a suitable generalization of the notion of s-sparse observability, previously introduced by some of the authors in the linear case. We also extend our previous work on the use of Satisfiability Modulo Theory solvers to estimate the state under sensor attacks to the context of differentially flat systems. The effectiveness of our approach is illustrated on the problem of controlling a quadrotor under sensor attacks.Comment: arXiv admin note: text overlap with arXiv:1412.432

Customer experience quality with social robots: Does trust matter?

Although service providers increasingly adopt social robots, much remains to be learned about what influences customers\u27 experiences with robots. To address this issue, this study investigates the relationships among customer equity drivers (i.e., value equity, brand equity and relationship equity), trust in social robots, and trust in service providers. Specifically, we hypothesize that customer equity drivers influence trust in social robots and trust in service providers. We also propose that customer equity drivers influence customer experience quality in the context of social robots and that trust in social robots and trust in service providers mediate these relationships. The study used a two-stage hybrid partial least squares structural equation modelling (PLS-SEM)-artificial neural network (ANN) analysis to examine the proposed relationships. Findings show that while all the customer equity drivers influence trust in service providers, only brand and relationship equity influence trust in social robots. Results also suggest that trust in service providers mediates the relationship between customer equity drivers and customer experience quality. In addition, we find that consumers\u27 trust in service providers helps generate trust in social robots. Theoretical and managerial implications are discussed

Relationship quality in customer-service robot interactions in industry 5.0: An analysis of value recipes

The paper studies the interactions between customers and robots within the framework of Industry 5.0-driven services. Prior studies have explored several factors contributing to the quality of these interactions, with perceived value being a crucial aspect. This study uses value recipes, which refer to specific configurations of how different benefits and costs are weighed up/evaluated, as a theoretical framework to investigate the quality of relationships between customers and service robots. The study aims to shed light on the complex interplay between different value dimensions that shape customers\u27 relationships with robots. To achieve this goal, the authors analyze what value configurations facilitate or impede high-quality relationships between customers and service robots. Fuzzy set qualitative comparative analysis (fsQCA) was used to analyze data from 326 consumers. The data reveal that value recipes comprising positive values (such as relational benefit, novelty, control, personalization, excellence, and convenience) and negative values (about privacy and effort) prove highly effective in augmenting relationship quality. Results also underscore those negative values either in isolation or in conjunction with positive values, do not impede relationship quality. The theoretical contribution of this study lies in presenting new insights into relationship dynamics between customers and service robots in an Industry 5.0 value-driven context. From a practical standpoint, the findings suggest guidelines for successfully infusing the retail landscape with more intelligent service robots

Political social media marketing:a systematic literature review and agenda for future research

We focus on political marketing and conduct a systematic literature review of journal articles exploring political marketing on social media. The systematic literature review delineates the current state of political social media marketing literature. It spans six databases and comprises sixty-six journal articles published between 2011 and 2020. We identify and categorize the variables studied in the literature and develop an integrative framework that links these variables. We describe the research themes that exist in the literature. The review demonstrates that the field is growing. However, the literature is fragmented, along with being predominantly based in the US context. Conceptual and theoretical shortcomings also exist. Moreover, the literature ignores pertinent contemporary topics such as co-creation, influencer marketing, and political advertising on social media. Nevertheless, a nascent domain with growing practical significance, political social media marketing provides various exciting avenues for further research, which we outline in this study.</p

On the Utility of Learning about Humans for Human-AI Coordination

While we would like agents that can coordinate with humans, current algorithms such as self-play and population-based training create agents that can coordinate with themselves. Agents that assume their partner to be optimal or similar to them can converge to coordination protocols that fail to understand and be understood by humans. To demonstrate this, we introduce a simple environment that requires challenging coordination, based on the popular game Overcooked, and learn a simple model that mimics human play. We evaluate the performance of agents trained via self-play and population-based training. These agents perform very well when paired with themselves, but when paired with our human model, they are significantly worse than agents designed to play with the human model. An experiment with a planning algorithm yields the same conclusion, though only when the human-aware planner is given the exact human model that it is playing with. A user study with real humans shows this pattern as well, though less strongly. Qualitatively, we find that the gains come from having the agent adapt to the human's gameplay. Given this result, we suggest several approaches for designing agents that learn about humans in order to better coordinate with them. Code is available at https://github.com/HumanCompatibleAI/overcooked_ai.Comment: Published at NeurIPS 2019 (http://papers.nips.cc/paper/8760-on-the-utility-of-learning-about-humans-for-human-ai-coordination

A Contract-Based Methodology for Aircraft Electric Power System Design

In an aircraft electric power system, one or more supervisory control units actuate a set of electromechanical switches to dynamically distribute power from generators to loads, while satisfying safety, reliability, and real-time performance requirements. To reduce expensive redesign steps, this control problem is generally addressed by minor incremental changes on top of consolidated solutions. A more systematic approach is hindered by a lack of rigorous design methodologies that allow estimating the impact of earlier design decisions on the final implementation. To achieve an optimal implementation that satisfies a set of requirements, we propose a platform-based methodology for electric power system design, which enables independent implementation of system topology (i.e., interconnection among elements) and control protocol by using a compositional approach. In our flow, design space exploration is carried out as a sequence of refinement steps from the initial specification toward a final implementation by mapping higher level behavioral and performance models into a set of either existing or virtual library components at the lower level of abstraction. Specifications are first expressed using the formalisms of linear temporal logic, signal temporal logic, and arithmetic constraints on Boolean variables. To reason about different requirements, we use specialized analysis and synthesis frameworks and formulate assume guarantee contracts at the articulation points in the design flow. We show the effectiveness of our approach on a proof-of-concept electric power system design

Higher order geometric flows on three dimensional locally homogeneous spaces

We analyse second order (in Riemann curvature) geometric flows (un-normalised) on locally homogeneous three manifolds and look for specific features through the solutions (analytic whereever possible, otherwise numerical) of the evolution equations. Several novelties appear in the context of scale factor evolution, fixed curves, phase portraits, approaches to singular metrics, isotropisation and curvature scalar evolution. The distinguishing features linked to the presence of the second order term in the flow equation are pointed out. Throughout the article, we compare the results obtained, with the corresponding results for un-normalized Ricci flows.Comment: to appear in Journal of Mathematical Physics (2013