The generalizability of empirical research depends on the reproduction of findings across settings and populations. Consequently, generalizations demand resources beyond that which is typically available to any one laboratory. With collective interest in the joint Simon effect (JSE)-a phenomenon that suggests people work more effectively with humanlike (as opposed to mechanomorphic) robots -we pursued a multi-institutional research cooperation between robotics researchers, social scientists, and software engineers. To evaluate the robustness of the JSE in dyadic human-robot interactions, we constructed an experimental infrastructure for exact, lab-independent reproduction of robot behavior. Deployment of our infrastructure across three institutions with distinct research orientations (well-resourced versus resource-constrained) provides initial demonstration of the success of our approach and the degree to which it can alleviate technical barriers to HRI reproducibility. Moreover, with the three deployments situated in culturally distinct contexts (Germany, the U.S. Midwest, and the Mexico-U.S. Border), observation of a JSE at each site provides evidence its generalizability across settings and populations. CCS CONCEPTS •Human-centered computing →Empirical studies in HCI. ACM Reference Format: Megan Strait, Florian Lier, Jasmin Bernotat, Sven Wachsmuth, Friederike Eyssel, Robert Goldstone, and Selma Šabanović. 2020. A Three-Site Reproduction of the Joint Simon Effect with the NAO Robot. In Proceedings of the 2020 ACM/IEEE Intemational Conference on Human-Robot Interaction (HRI ’ 20), March 23-26, 2020, Cambridge, United Kingdom. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3319502.3374783

In a rapidly developing IT business environment, quality management practices in various forms are inevitable. Information Technology industry is experiencing the fastest growth in the Bosnian economy over the past five years. Therefore, they are constantly adapting to meet the challenges of digital transformation and to satisfy the expectations of today’s customers. The best way in which an organization undertakes business activities is through quality management practices and organizational learning, which improves product quality and reduce product returns and the cost of servicing dissatisfied customers. This approach ultimately leads to an improvement in the company’s performance. This study proposes a research model based on extensive literature review. This model may serve as a good basis to investigate interrelationships between TQM practices, organizational learning, and organizational performance. It may also help to determine if organizational learning fosters plays a mediating role between TQM practices and performance in IT Sector. Further validation of the model is strongly recommended to future researchers.

Mathematical modelling to compute ground truth from 3D images is an area of research that can strongly benefit from machine learning methods. Deep neural networks (DNNs) are state-of-the-art methods design for solving these kinds of difficulties. Convolutional neural networks (CNNs), as one class of DNNs, can overcome special requirements of quantitative analysis especially when image segmentation is needed. This article presents a system that uses a cascade of CNNs with symmetric blocks of layers in chain, dedicated to 3D image segmentation from microscopic images of 3D nuclei. The system is designed through eight experiments that differ in following aspects: number of training slices and 3D samples for training, usage of pre-trained CNNs and number of slices and 3D samples for validation. CNNs parameters are optimized using linear, brute force, and random combinatorics, followed by voter and median operations. Data augmentation techniques such as reflection, translation and rotation are used in order to produce sufficient training set for CNNs. Optimal CNN parameters are reached by defining 11 standard and two proposed metrics. Finally, benchmarking demonstrates that CNNs improve segmentation accuracy, reliability and increased annotation accuracy, confirming the relevance of CNNs to generate high-throughput mathematical ground truth 3D images.

In this paper, two approaches are evaluated using the Full Error Detection and Correction (FEDC) method for a pipelined structure. The approaches are referred to as Full Duplication with Comparison...

Few-layer (FL) transition metal dichalcogenides have drawn attention for nanoelectronics applications due to their improved mobility, owing to the partial screening of charged impurities at the oxide interface. However, under realistic operating conditions, dissipation leads to self-heating, which is detrimental to electronic and thermal properties. We fabricated a series of FL-WSe2 devices and measured their I-V characteristics, while their temperatures were quantified by Raman thermometry and simulated from first principles. Our tightly-integrated electro-thermal study shows that Joule heating leads to a significant layer-dependent temperature rise, which affects mobility and alters the flow of current through the stack. This causes the temperatures in the top layers to increase dramatically, degrading their mobility and causing the current to reroute to the bottom of the FL stack where thermal conductance is higher. We discover that this current rerouting phenomenon improves heat removal because the current flows through layers closer to the substrate, limiting the severity of self-heating and its impact on carrier mobility. We also observe significant lateral heat removal via the contacts because of longer thermal healing length in the top layers and explore the optimum number of layers to maximize mobility in FL devices. Our study will impact future device designs and lead to further improvements in thermal management in vdW-based devices.

Given the application of a multiple regression and artificial neural networks (ANNs), this paper describes development of models for predicting surface roughness, linking an arithmetic mean deviation of a surface roughness to a torque as an input variable, in the process of drilling enhancement steel EN 42CrMo4, thermally treated to the hardness level of 28 HRC, using cruciform blade twist drills made of high speed steel with hardness level of 64–68 HRC. The model was developed using process parameters (nominal diameters of twist drills, speed, feed, and angle of installation of work pieces) as input variables varied at three levels by Taguchi design of experiment and measured experimental data for a torque and arithmetic mean deviation of a surface roughness for different values of flank wear of twist drills. The comparative analysis of the models results and the experimental data, acquired for the inputs at the moment when a wear span reaches a limit value corresponding to a moment of the drills blunting, demonstrates that the neural network model gives better results than the results obtained in the application of multiple linear and nonlinear regression models.

The quantum-mechanical transition amplitude of an ionization process induced by a strong laser field is typically expressed in the form of an integral over the ionization time of a highly oscillatory function. Within the saddle-point (SP) approximation this integral can be represented by a sum over the contributions of the solutions of the SP equation for complex ionization time. It is shown that, for the general case of an elliptically polarized polychromatic laser field, these solutions can be obtained as zeros of a trigonometric polynomial of the order n and that there are exactly n relevant solutions, which are to be included in the sum. The results obtained are illustrated by examples of various tailored laser fields that are presently used in strong-field physics and attoscience. For some critical values of the parameters two SP solutions can coalesce and the topology of the ‘steepest descent’ integration contour changes so that some SPs are bypassed. Around the critical parameters a uniform approximation should be used instead of the SP method.