Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating User eXperience as a Means to Reveal the Potential Adoption of Innovative Ideas

This paper presents an empirical study dedicated to the evaluation of user experience (UX) and its possible causal influences on adoption. The empirical study was conducted in the context of an Innovation Management track in Fall 2019, where student teams had the opportunity to co-create innovative mobile app ideas. The applied UX-based adoption model, which in previous studies, has undergone numerous limited scale reliability, validity, and goodness of fit tests, was initiated through the first tentative model created in 2016. For each mobile app idea, a bipolar survey allowed the collection of both quantitative and qualitative data in order to evaluate the degree of UX satisfaction and its causal effect on adoption. This new study offers limited, early-stage evidence for possible cross-validation of the UX multidimensional aspect of the model by demonstrating that several UX facets, belonging to different dimensions, directly influence the anticipated UX and intention to adopt in the context of eight unique, innovative mobile app ideas. It also reveals that this approach could be used to sift through innovative ideas during the Fuzzy Front End stage in predicting their potential adoption success at the later close-to-market and on-the-market stages. Finally, it also provides some empirical evidence to support the hypothesis that the higher the degree of UX satisfaction, the stronger the intention to adopt.
Magnetic miniature swimmers with multiple rigid flagella

In this paper, we introduce novel miniature swimmers with multiple rigid tails based on spherical helices. The tail distribution of these prototypes enhances its swimming features as well as allowing to carry objects with it. The proposed swimmers are actuated by a rotating magnetic field, generating the robot rotation and thus producing a considerable thrust to start self-propelling. These prototypes achieved propulsion speeds up to 6 mm/s at 3.5 Hz for a 6-mm in size prototypes. We study the efficiency of different tail distribution for a 2-tailed swimmer by varying the angular position between both tails. Moreover, it is demonstrated that these swimmers experience great sensibility when changing their tail height. Besides, these swimmers demonstrate to be effective for cargo carrying tasks since they can displace objects up to 3.5 times their weight. Finally, wall effect is studied with multi-tailed swimmer robots considering 2 containers with 20 and 50-mm in width. Results showed speeds’ increments up to 59% when swimmers are actuated in the smaller container.
Fast Multichannel Nonnegative Matrix Factorization With Directivity-Aware Jointly-Diagonalizable Spatial Covariance Matrices for Blind Source Separation

This article describes a computationally-efficient blind source separation (BSS) method based on the independence, low-rankness, and directivity of the sources. A typical approach to BSS is unsupervised learning of a probabilistic model that consists of a source model representing the time-frequency structure of source images and a spatial model representing their inter-channel covariance structure. Building upon the low-rank source model based on nonnegative matrix factorization (NMF), which has been considered to be effective for inter-frequency source alignment, multichannel NMF (MNMF) assumes source images to follow multivariate complex Gaussian distributions with unconstrained full-rank spatial covariance matrices (SCMs). An effective way of reducing the computational cost and initialization sensitivity of MNMF is to restrict the degree of freedom of SCMs. While a variant of MNMF called independent low-rank matrix analysis (ILRMA) severely restricts SCMs to rank-1 matrices under an idealized condition that only directional and less-echoic sources exist, we restrict SCMs to jointly-diagonalizable yet full-rank matrices in a frequency-wise manner, resulting in FastMNMF1. To help inter-frequency source alignment, we then propose FastMNMF2 that shares the directional feature of each source over all frequency bins. To explicitly consider the directivity or diffuseness of each source, we also propose rank-constrained FastMNMF that enables us to individually specify the ranks of SCMs. Our experiments showed the superiority of FastMNMF over MNMF and ILRMA in speech separation and the effectiveness of the rank constraint in speech enhancement.
A 3-D Simulation-Based Approach to Analyze Heavy Ions-Induced SET on Digital Circuits

Radiation-induced single-event transients (SETs) are the leading cause of mal-operations in CMOS nanometric integrated circuits. The increasing complexity of advanced CMOS digital circuits makes SET effects investigation a rising challenge. In this work, we propose a 3-D-oriented simulation approach able to model the passage of heavy ion particles through the physical structures of modern digital circuits implemented with ultrananometric manufacturing processes. The proposed approach is able to generate the transient voltage pulse in response to a heavy-ion track and identify the effects of the sensitive volume and contact structure. We present heavy-ion radiation test experiments performed on a 65-nm Flash-based CMOS technology process and, as proof-of-concept, we successfully compared the SET cross sections showing comparable results.
Multiphase Open-End Winding Induction Machine Drive With the Floating Capacitor

A comprehensive control scheme is presented for the multiphase open-end winding induction machine drive topology, where the main inverter (MI) and floating inverter (FI) are supplied by a dc power source and a capacitor, respectively. Under the proposed method, the MI can work with the unity power factor, while the FI provides all the reactive power. Besides, the current waveform quality is improved by the harmonic suppression and current balance control. Moreover, the voltage of the floating capacitor is adjusted to keep the modulation ratio of the FI near one during the operation, which helps to reduce the switching losses of the system. Experimental results are presented to verify that the proposed control scheme is practical in controlling this open-end drive topology and suitable for expansion to high-phase-number occasions.
Hierarchical 6-DoF Grasping with Approaching Direction Selection

In this paper, we tackle the problem of 6-DoF grasp detection which is crucial for robot grasping in cluttered real-world scenes. Unlike existing approaches which synthesize 6-DoF grasp data sets and train grasp quality networks with input grasp representations based on point clouds, we rather take a novel hierarchical approach which does not use any 6-DoF grasp data. We cast the 6-DoF grasp detection problem as a robot arm approaching direction selection problem using the existing 4-DoF grasp detection algorithm, by exploiting a fully convolutional grasp quality network for evaluating the quality of an approaching direction. To select the best approaching direction with the highest grasp quality, we propose an approaching direction selection method which leverages a geometry-based prior and a derivative-free optimization method. Specifically, we optimize the direction iteratively using the cross entropy method with initial samples of surface normal directions. Our algorithm efficiently finds diverse 6-DoF grasps by the novel way of evaluating and optimizing approaching directions. We validate that the proposed method outperforms other selection methods in scenarios with cluttered objects in a physics-based simulator. Finally, we show that our method outperforms the state-of-the-art grasp detection method in real-world experiments with robots.
Hierarchical Quadtree Feature Optical Flow Tracking Based Sparse Pose-Graph Visual-Inertial SLAM

Accurate, robust and real-time localization under constrained-resources is a critical problem to be solved. In this paper, we present a new sparse pose-graph visual-inertial SLAM (SPVIS). Unlike the existing methods that are costly to deal with a large number of redundant features and 3D map points, which are inefficient for improving positioning accuracy, we focus on the concise visual cues for high-precision pose estimating. We propose a novel hierarchical quadtree based optical flow tracking algorithm, it achieves high accuracy and robustness within very few concise features, which is only about one fifth features of the state-of-the-art visual-inertial SLAM algorithms. Benefiting from the efficient optical flow tracking, our sparse pose-graph optimization time cost achieves bounded complexity. By selecting and optimizing the informative features in sliding window and local VIO, the computational complexity is bounded, it achieves low time cost in long-term operation. We compare with the state-of-the-art VIO/VI-SLAM systems on the challenging public datasets by the embedded platform without GPUs, the results effectively verify that the proposed method has better real-time performance and localization accuracy.
Applying A User eXperience-based Adoption Model in Several App Idea Cases

This paper presents an empirical study dedicated to the evaluation of User eXperience (UX) and its impact on potential adoption. This empirical study, which was carried out in Fall 2019, took place in the context of exploring the transferability of a previously validated mixed-methods survey instrument and the underlying formative UX model. Several app idea co-creation experiments were conducted in the context of an Innovation Management course where student teams had to co-create mobile app ideas. The resulting formative model was operationalized in the form of a mixed-methods bipolar survey instrument. Based on empirical evidence in the form of 328 responses from the same population of students participating in the eight distinct app idea co-creation experiments, the instantiated and refined UX model has met multiple criteria of reliability, validity, and goodness of fit. This study demonstrates that several UX facets, belonging to different dimensions, directly influence the anticipated UX satisfaction and the related degree of adoption. In particular, the high value of the coefficient of determination R2 = 0.707 shows, that almost 71% of the variability of Intention-to-Adopt is explained by User-Experience. The study provides early-stage evidence of possible transferability of the UX-based adoption model in evaluation of both the UX satisfaction and its impact on the adoption of any product, service or bundle idea in either concept, prototype or ready-to-market stage of the New Product Development cycle.
Hydrodynamic modelling and characterisation of a shallow fluvial lake: a study on the Superior Lake of Mantua

This paper presents a numerical modelling framework developed to simulate circulations and to generally characterise the hydrodynamics of the Superior Lake of Mantua, a shallow fluvial lake in Northern Italy. Such eutrophied basin is characterised by low winds, reduced discharges during the summer and by the presence of large lotus flower ( Nelumbo nucifera ) meadows, all contributing to water stagnation. A hydrodynamic numerical model was built to understand how physical drivers shape basic circulation dynamics, selecting appropriate methodologies for the lake. These include a 3D code to reproduce the interaction between wind and through-flowing current, a fetch-dependent wind stress model, a porous media approach for canopy flow resistance and the consideration of wave-current interaction. The model allowed to estimate the circulation modes and water residence time distributions under identified typical ordinary, storm and drought conditions, the hydrodynamic influence of the newly-opened secondary outlet of the lake, the surface wave parameters, their influence on circulations and the bottom stress they originate, and the adaptation time scales of circulations to storm events. Some probable effects of the obtained hydrodynamic characteristics of the Superior Lake of Mantua on its biochemical processes are also introduced.
Controlling an upper-limb exoskeleton by EMG signal while carrying unknown load

Implementing an intuitive control law for an upper-limb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to assist carrying an unknown load without using force sensors or specific handle. The method is based on user's intentions estimated through a wireless EMG armband allowing movement direction and intensity estimation along 1 Degree of Freedom. This control law aimed to behave like a gravity compensation except that the mass of the load does not need to be known. The proposed approach was tested on 10 participants during a lifting task with a single Degree of Freedom upper-limb exoskeleton. Participants performed it in three different conditions : without assistance, with an exact gravity compensation and with the proposed method based on EMG armband. The evaluation of the efficiency of the assistance was based on EMG signals captured on seven muscles (objective indicator) and a questionnaire (subjective indicator). Results showed a statically significant reduction of mean activity of the biceps, erector spinae and deltoid by 20%±14%, 18%±12% and 25% ± 16% respectively while comparing the proposed method with no assistance. In addition, similar muscle activities were found both in the proposed method and the traditional gravity compensation. Subjective evaluation showed better precision, efficiency and responsiveness of the proposed method compared to the traditional one.
Internet of Things (IoT)-based Collaborative Control of a Redundant Manipulator for Teleoperated Minimally Invasive Surgeries

In this paper, an Internet of Things-based human-robot collaborative control scheme is developed in Robot-assisted Minimally Invasive Surgery scenario. A hierarchical operational space formulation is designed to exploit the redundancies of the 7-DoFs redundant manipulator to handle multiple operational tasks based on their priority levels, such as guaranteeing a remote center of motion constraint and avoiding collision with a swivel motion without influencing the undergoing surgical operation. Furthermore, the concept of the Internet of Robotic Things is exploited to facilitate the best action of the robot in human-robot interaction. Instead of utilizing compliant swivel motion, HTC VIVE PRO controllers, used as the Internet of Things technology, is adopted to detect the collision. A virtual force is applied to the robot elbow, enabling a smooth swivel motion for human-robot interaction. The effectiveness of the proposed strategy is validated using experiments performed on a patient phantom in a lab setup environment, with a KUKA LWR4+ slave robot and a SIGMA 7 master manipulator. By comparison with previous works, the results show improved performances in terms of the accuracy of the RCM constraint and surgical tip.
Improving the Accuracy of FE Simulations of Induction Tempering Toward a Microstructure-Dependent Electromagnetic Model

Tempering is a heat treatment process that is used to increase the toughness of hardened steels. Compared with the conventional treatment, induction tempering has the advantage of dramatically reducing the heating time. Shorter times, however, require higher temperatures, of up to 450 °C, depending on the tempering step. Numerical simulations of induction tempering require accurate electromagnetic modeling since the entire heating occurs below the Curie point. In this article, the single-valued <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> curves of a pre-hardened and hardened steel are determined using an inverse approach. Second, an equivalent <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> curve that can be used in the time-harmonic analysis is provided. It is studied which modeling simplifications are possible without significantly affecting the quality of simulation results.
Study of Consumer Intentions on Using Mobile Wallets Using TAM Model

Digital payments are the future. In India digital payments have raced up post demonetisation in November 2016. Convenience is the major factor which is drawing consumers towards digital payments. Mobile wallets not only give convenience to the consumers in making payments but also gives them additional benefits like cashbacks and some extra discounts. This study focuses on studying the consumer intentions on using M-wallets via technology acceptance model (TAM). The sample size taken was 186 from Delhi NCR and Lucknow, India. The methodology used in the study is descriptive in nature, convenience sampling technique was used to accumulate the data. Perceived ease of use, Perceived usefulness, Attitude were found to be significantly associated and having a positive linear relationship with the Behavioural intentions as per the correlation and regression analysis whereas Perceived security was not having positive linear relationship with the Behavioural intentions as per the regression analysis since security is an inherent feature of Mobile wallets.
Whole-grain rye foods: effects on appetite and metabolism

Overweight, obesity and associated diet-related chronic diseases are increasing worldwide. Diet constitutes a modifiable risk factor for these conditions. Whole-grain consumption has been associated with health benefits, e.g. decreased risk of weight gain, diabetes and cardiovascular disease. Furthermore, whole-grain rye foods, rich in dietary fibres, have upon consumption shown increased satiety and lower postprandial glycaemia and insulinaemia compared with refined wheat. The overall aim of the present thesis was to investigate how appetite and postprandial glycaemia and insulinaemia were affected by consumption of whole-grain rye porridge and crisp bread compared with isocaloric refined wheat bread, served as part of complete breakfasts. Effects of replacing part of the rye in whole-grain rye porridge with inulin and wheat gluten were evaluated on appetite, food intake, gut fermentation, postprandial glucose, insulin and GLP-1 during 8 h after intake. The effect of daily consumption of whole-grain rye porridge for three weeks was evaluated on appetite, food intake, gut fermentation and gut passage time during 8-12 h after intake. Moreover, the impact of unfermented and yeast-fermented whole-grain rye crisp bread on appetite, postprandial glucose and insulin responses was evaluated during 4 h after intake. Whole-grain rye porridge reduced appetite during 4 h after intake compared with refined wheat bread, even after three weeks of daily consumption. The satiating effect of whole-grain rye porridge did not increase with added inulin and wheat gluten. The extensive gut fermentation of whole-grain rye porridge 4-8 h after intake was related to a lower second meal glucose response, but not to appetite, and no differences were observed in postprandial insulin and GLP-1 responses compared with refined wheat bread. Whole-grain rye crisp bread reduced appetite and postprandial insulin response during 4 h after intake compared with refined wheat crisp bread, with no differences in postprandial glucose, and the effect was larger for unfermented whole-grain rye crisp bread. To conclude, whole-grain rye porridge and crisp bread have beneficial effects on appetite regulation and metabolic responses. Including whole-grain rye foods as part of a healthy diet may contribute to appetite control and decrease the risk of diet-related chronic diseases.
Study on Reactor Neutrino Directionality Search Utilizing Vertex Information Reconstructed by PMT Operating State in a Liquid Scintillator Detector

The properties of neutrinos are not well known because they weakly interact with surrounding matter. Recently, several interesting results and phenomena have been measured using reactor neutrinos. Although there are various types of detectors that detect neutrino signals, they generally use photomultiplier tubes (PMTs) to collect data. In particular, signals obtained from each PMT surrounding a detector are used to reconstruct a vertex of an incident neutrino in the detector. However, if a number of PMTs are out of operation from the initial installation, the performance of the PMT or its operation efficiency may be changed over time. This may affect the information for the reconstructed vertex of an incident neutrino, which can have a direct impact on results that utilize the reconstructed vertex information. This article briefly describes the change of reconstructed vertex according to operating status of PMTs around the reactor neutrino detector by using a Monte Carlo (MC) simulation based on a cylindrical detector equipped with liquid scintillator. The MC simulation for this analysis needs a moderate number of central processing units (CPUs) and cores. Therefore, various resources and servers at a supercomputing center were used. Our results can be used to more accurately conduct research related to neutrino direction using PMT information in the future.
Design of a 5G Network Slice Extension With MEC UAVs Managed With Reinforcement Learning

Network slices for delay-constrained applications in 5G systems require computing facilities at the edge of the network to guarantee ultra-low latency in processing data flows generated by connected devices, which is challenging with larger volumes of data, and larger distances to the edge of the network. To address this challenge, we propose to extend 5G network slices with Unmanned Aerial Vehicles (UAV) equipped with multi-access edge computing (MEC) facilities. However, onboard computing elements (CE) consume UAV’s battery power thus impacting its flight duration. We propose a framework where a System Controller (SC) can turn on and off UAV’s CEs, with the possibility of offloading jobs to other UAVs, to maximize an objective function defined in terms of power consumption, job loss, and incurred delay. Management of this framework is achieved by reinforcement learning. A Markov model of the system is introduced to enable reinforcement learning and provide guidelines for the selection of system parameters. A use case is considered to demonstrate the gain achieved by the proposed framework and discuss numerical results.
RISE: A Novel Indoor Visual Place Recogniser

This paper presents a new technique to solve the Indoor Visual Place Recognition problem from the Deep Learning perspective. It consists on an image retrieval approach supported by a novel image similarity metric. Our work uses a 3D laser sensor mounted on a backpack with a calibrated spherical camera i) to generate the data for training the deep neural network and ii) to build a database of geo-referenced images for an environment. The data collection stage is fully automatic and requires no user intervention for labelling. Thanks to the 3D laser measurements and the spherical panoramas, we can efficiently survey large indoor areas in a very short time. The underlying 3D data associated to the map allows us to define the similarity between two training images as the geometric overlap between the observed pixels. We exploit this similarity metric to effectively train a CNN that maps images into compact embeddings. The goal of the training is to ensure that the L2 distance between the embeddings associated to two images is small when they are observing the same place and large when they are observing different places. After the training, similarities between a query image and the geo-referenced images in the database are efficiently retrieved by performing a nearest neighbour search in the embeddings space.