Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study and a New Industrial Platform for Decentralized Anomaly Detection Using Machine Learning Algorithms

The occurrence of anomalies and unexpected, process-related faults is a major problem for manufacturing systems, which has a significant impact on product quality. Early detection of anomalies is therefore of central importance in order to create sufficient room for maneuver to take countermeasures and ensure product quality. This paper investigates the performance of machine learning (ML) algorithms for anomaly detection in sensor data streams. For this purpose, the performance of six ML algorithms (K-means, DBSCAN, Isolation Forest, OCSVM, LSTM-Network, and DeepAnt) is evaluated based on defined performance metrics. These methods are benchmarked on publicly available datasets, own synthetic datasets, and novel industrial datasets. The latter include radar sensor datasets from a hot rolling mill. Research results show a high detection performance of K-means algorithm, DBSCAN algorithm and LSTM network for punctual, collective and contextual anomalies. A decentralized strategy for (real-time) anomaly detection using sensor data streams is proposed and an industrial (Cloud-Edge Computing) platform is developed and implemented for this purpose.
Quantum Gravity phenomenology: achievements and challenges

Motivated by scenarios of quantum gravity, Planck-suppressed deviations from Lorentz invariance are expected at observable energies. Ultra-High-Energy Cosmic Rays, the most energetic particles ever observed in nature, yielded in the last two years strong constraints on deviations suppressed by O(E2/M2Pl) and also, for the first time, on space-time foam, stringy inspired models of quantum gravity. We review the most important achievements and discuss future outlooks.
With a Little Help of Humans. An Exploratory Study of Delivery Robots Stuck in Snow

People’s willingness to help robots has been explored in the lab and in the wild in various settings. While previous studies relied on robotic prototypes, service robots are now already deployed in public spaces. This presents a novel and exciting opportunity for human-robot interaction (HRI) scholars to study robotic technologies in the context of their deployment. In this paper, we present the qualitative methodology and the outcomes of an exploratory mixed-methods (observations, autoethnography, online content analysis) study of people voluntarily helping commercially deployed delivery robots in Tallinn, Estonia. Based on the cumulative findings of the three methods, we discuss how spontaneous help towards robots manifested, the situational factors that may have contributed to the observed helping behaviors, and the role that the perceptions of the robots as cute and helpful may have played in these interactions. While our findings support the assumption that human help is a reasonable mitigation strategy to overcoming the challenges service robots may face in uncontrolled environments, we also emphasize the importance of considering the ethical implications when commercial technology relies in part on passersby to succeed in its tasks.
Time-aware Neural Trip Planning Reinforced by Human Mobility

Trip planning, which targets at planning a trip consisting of several ordered Points of Interest (POIs) under user-provided constraints, has long been treated as an important application for location-based services. The goal of trip planning is to maximize the chance that the users will follow the planned trip while it is difficult to directly quantify and optimize the chance. Conventional methods either leverage statistical analysis to rank POIs to form a trip or generate trips following pre-defined objectives based on constraint programming to bypass such a problem. However, these methods may fail to reflect the complex latent patterns hidden in the human mobility data. On the other hand, though there are a few deep learning-based trip recommendation methods, these methods still cannot handle the time budget constraint so far. To this end, we propose a TIme-aware Neural Trip Planning (TINT) framework to tackle the above challenges. First of all, we devise a novel attention-based encoder-decoder trip generator that can learn the correlations among POIs and generate trips under given constraints. Then, we propose a specially-designed reinforcement learning (RL) paradigm to directly optimize the objective to obtain an optimal trip generator. For this purpose, we introduce a discriminator, which distinguishes the generated trips from real-life trips taken by users, to provide reward signals to optimize the generator. Subsequently, to ensure the feedback from the discriminator is always instructive, we integrate an adversarial learning strategy into the RL paradigm to update the trip generator and the discriminator alternately. Moreover, we devise a novel pre-training schema to speed up the convergence for an efficient training process. Extensive experiments on four real-world datasets validate the effectiveness and efficiency of our framework, which shows that TINT could remarkably outperform the state-of-the-art baselines within short response time.
Outer density profiles of 19 Galactic globular clusters from deep and wide-field imaging

Using deep photometric data from WFC@INT and WFI@ESO2.2m we measure the outer number density profiles of 19 stellar clusters located in the inner region of the Milky Way halo (within a Galactocentric distance range of 10-30 kpc) in order to assess the impact of internal and external dynamical processes on the spatial distribution of stars. Adopting power-law fitting templates, with index $-\gamma$ in the outer region, we find that the clusters in our sample can be divided in two groups: a group of massive clusters ($ \ge 10^5 $ M_sun) that has relatively flat profiles with $2.5 4$) and clear signatures of interaction with the Galactic tidal field. We refer to these two groups as 'tidally unaffected' and 'tidally affected', respectively. Our results also show a clear trend between the slope of the outer parts and the half-mass density of these systems, which suggests that the outer density profiles may retain key information on the dominant processes driving the dynamical evolution of Globular Clusters.
Towards evaluating the impact of swarm robotic control strategy on operators’ cognitive load

The use of multi-robot systems is increasing in disaster response, industry, transport, and logistics. Humans will remain indispensable to control and manage these fleets of robots, particularly in safety-critical applications. However, a human operator’s cognitive capacities can be challenged and exceeded as the sizes of autonomous fleets grow, and more sophisticated AI techniques can lead to opaque robot control programs. In a user study (n = 40), we explore how autonomous swarm intelligence algorithms and novel tangible interaction modalities relate to subjective and physiological indices of operator cognitive load (i.e., NASA Task Load Index, heart rate variability). Our findings suggest that there are differences in workload across conditions; however, subjective and cardiac measures appear to be sensitive to different aspects of cognitive state. The results hint at the potential of both tangible interfaces and automation to engage operators and reduce cognitive load, yet show the need for further validation of workload measures for use in studying and optimizing human-swarm interactions.
Ternary Change Detection in SAR Images Based on Bi-hierarchical SDAE and Bayesian Optimization

In this paper, we propose a new change detection method of multi-temporal synthetic aperture radar (SAR) images. Due to the ability of extracting key feature of images and robustness to noise, stacked denoising auto encoder (SDAE) has been widely used in remote sensing. However, the single SDAE stills has some limitations to handle with the speckle noise of SAR images. Therefore, we propose a new structure Bi-hierarchical SDAE for feature extraction. The first level of SDAE denoises the original image and reconstructs the difference map, and the second level extracts the superpixel-based difference features for classification. Besides, Bayesian optimization effectively improves the classification performance of feature classifier. The experimental results of the datasets in this paper show that the Bi-hierarchical SDAE and Bayesian optimization framework has high accuracy and proves its effectiveness.
Measuring Visual Social Engagement from Proxemics and Gaze

When we approach a group, there is an exchange of a multitude of verbal or non-verbal social signals to indicate that we are looking to interact. We continue to share these signals throughout the interaction to portray our thoughts and motivations. We define an interaction by the signals we send; sending different signals evokes a different response. Giving social robots the knowledge of group social interaction, they will have the ability to more effectively participate in these interactions in the real world. In this paper, we present the results from an online data collection study looking at social group dynamics. We collected a dataset of social behaviours in a group using a socially interactive game played online by 88 participants. We also introduce a novel visual social engagement metric, which is derived from two social signals: proxemics (distance between interaction participants) and mutual gaze. We propose a mathematical formula of both mutual gaze as the product of the mutual distances to the optical axis, and the visual social engagement as mutual gaze divided by distance between participants. Additionally, we investigate the influence of personality traits on the resulting interaction patterns. Using the metric, we create unique interaction profiles which suggest that participants have an interaction ‘style’. No clear correlation between personality and interaction patterns was found.
Chemical Property Relation Guided Few-Shot Molecular Property Prediction

The ability of molecular property prediction is of great significance to drug discovery. However, molecular property prediction is essentially a few-shot problem, making it a challenge in deep learning applications in this scenario. Although some deep learning models, such as meta-learning, have been used in few-shot molecular property prediction, they neglect the relationships among different properties. In this paper, we introduce a chemical property relation modeling technique for generating the property relation map, which can guide the few-shot molecular property prediction with positively related properties. Some molecules sharing common properties are firstly picked out. Then, multiple property-aware graph neural networks are trained for extracting molecular representations from those molecules. Next, the Spearman's correlation is adopted to calculate the property relations with the property-aware matrix, which is built by calculating the pairwise similarity of the above molecular representations for each pair of properties. In the few-shot molecular property prediction task, the meta-learning strategy is adopted to learn common prediction knowledge from the meta-training categories, which are provided by the property relations. Extensive experiments on two public few-shot molecular property datasets demonstrate that the positively related properties are beneficial for the target property prediction, while negatively related properties have negative effects. With the guidance of positively related properties, the proposed method outperforms various state-of-the-art methods.
Auto-Encoding Independent Attribute Transformation for Naive Bayesian Classifier

To alleviate the attribute independence assumption, this paper proposes an auto-encoding naive Bayesian classifier (AE-NBC) which is trained based on transformed independent condition-attributes using an auto-encoding strategy. First, an auto-encoding neural network (AENN) with single hidden-layer is created to map the original data set to a high-dimensional data space. To guarantee the independence among auto-encoding condition-attributes (AECAs), an efficient and convergent objective function is elaborated to optimize the input-layer and output-layer weights of the AENN by considering both the auto-encoding error and attribute independence degree. Second, independent component analysis is iteratively carried out to remove the redundant attributes in AECAs so that the optimal transformed condition-attributes (TCAs) are obtained for the training of the NBC. Experiments were conducted on 29 benchmark data sets to validate the rationality, feasibility, and effectiveness of AE- NBC. Experimental results show that the independence among TCAs is stronger than among the original condition-attributes, and that AE- NBC can obtain higher training and testing accuracies than six Bayesian classifiers.
Digital design demonstration of 10kV SiC-MOSFET power module to improve wire-bonding layout for power cycle capabilities

Determining the reliability of 10 kV SiC-MOSFET power modules experimentally, is challenging due to the low number of samples available in the market. A digital design based on 3D thermal calculations is demonstrated to improve reliability capability in a 10 kV SiC-MOSFET power module. The module design was determined based on the calculation with digital twin modelling. The correctness of the digital twin model was confirmed by fabricating a 10kV SiC-MOSFETs power module sample and comparing the calculation temperature with the measured results. The design was focused on aluminum wires on chips, and the effect of the wire temperature by changing the wire layout was clarified. The results show that the improved wire layout reduces the wire temperature by 2.2-5.3% compared to the conventional design. This is expected to increase the power cycle capability by up to 31%, according to predictions based on the Coffin-Manson model.
The Effects of Interaction Strategy and Robot Intent on Shopping Behavior

There is a growing interest in the retail industry to deploy service robots for customer interactions. Deploying such customer-facing robots raises the question of how we want to interact with these robots and reveals concerns that businesses and marketers could use robots to manipulate consumers. In this experiment, 67 study participants interacted with different virtual shopping robots that tried to impact "shoppers" purchasing decisions. The results indicate that a robot can increase consumer spending. The study exemplifies how a collaborative robot could be used as a customer-serving robot in a retail environment and investigates the impact of (i) different interaction strategies (human vs robot control) and (ii) dark patterns on shopping behavior (manipulative vs supportive robot).
Personalized Gap Selection Algorithm for On-ramp Merging Based on Virtual Game

On-ramp merging is a challenging task to tackle for driving intelligence for high-level automated driving, in which selecting a suitable gap is vital for safety and efficiency. To take the oncoming dynamic interaction with other vehicles into consideration, a virtual game method including states prediction and level-k gaming is proposed, which can filter gaps inappropriate to merge to in advance. Then a gap selection algorithm based on utility with personalized parameters is used to select the best gap after the exclusion of virtual games. Driver-in-the-loop experiments are carried out to collect human driving data, which is used for personalized parameters calibration and validation. Test cases show that the gap selected by the well-calibrated algorithm is mostly consistent with different human drivers, reflecting the personalization ability.
Lane Detection Based on Deep Learning and SSIM Method

Lane detection is an important part of autonomous driving techniques and is required to have high accuracy and robustness. However, due to the complicated change of weather and lighting, environmental effects such as fog, and the shape of the straight lane and curved lane, the application scenarios of lane detection are limited. To solve the above problems, we propose a novel lane detection method using deep learning and SSIM method to aim at challenging scenarios. The proposed method can detect lane using two deep learning detection methods in parallel. Then using the structural similarity index measure (SSIM) image similarity detection method to compare with the labeled actual lanes from ground truth and select the more accurate result as the output. Experiments showed that the lane recognition rate is high, and the speed is fast in various complex scenarios. The proposed method can improve the accuracy and robustness of lane detection.
An effective chiral Hadron-Quark Equation of State Part I: Zero baryochemical potential

We construct an effective model for the QCD equation of state, taking into account chiral symmetry restoration as well as the deconfinement phase transition. The correct asymptotic degrees of freedom at the high and low temperature limits are included (quarks $\leftrightarrow$ hadrons). The model shows a rapid crossover for both order parameters, as is expected from lattice calculations. We then investigate the thermodynamic properties of the model at $\mu_B=0$. All thermodynamic quantities are in qualitative agreement with lattice data, while apparent quantitative differences can be attributed to hadronic contributions and excluded volume corrections.
Dimensional flow in the kappa-deformed spacetime

We derive the modified diffusion equations defined on kappa-space-time and using these, investigate the change in the spectral dimension of kappa-space-time with the probe scale. These deformed diffusion equations are derived by applying Wick's rotation to the $\kappa$-deformed Schr$\ddot{o}$dinger equations obtained from different choices of Klein-Gordon equations in the $\kappa$-deformed space-time. Using the solutions of these equations, obtained by perturbative method, we calculate the spectral dimension for different choices of the generalized Laplacian and analyse the dimensional flow in the $\kappa$-space-time. In the limit of commutative space-time, we recover the well known equality of spectral dimension and topological dimension. We show that the higher derivative term in the deformed diffusion equations make the spectral dimension unbounded (from below) at high energies. We show that the finite mass of the probe results in the spectral dimension to become infinitely negative at low energies also. In all the cases, we have analysed the effect of finite size of the probe on the spectral dimension.
Radiation Pattern Diversified Double-Fluid-Channel Surface-Wave Antenna for Mobile Communications

In this paper, we present an antenna design for millimeter wave 5G applications. The proposed antenna has a wide working frequency range from 23.5 GHz to 36.5 GHz. This can cover the millimeter wave 5G frequency band in most countries. The design is simple and will mitigate the difficulty when implementing in a wireless system with reconfigurable capability. The antenna design only needs a single RF port as input to achieve radiation pattern diversity by moving the fluid radiators in its two channels. With the radiation pattern diversity capability, the problem like weak signal strength and inter channel interference can be eased.The design shows higher dynamic range of patterns turning when compare to the previous work on single-channel surface-wave antenna with the purpose of wider angular coverage with multichannel design. The comparison result of the two designs will also be provided in this paper.
Productive Inconvenience: Facilitating Posture Variability by Stimulating Robot-to-Human Handovers

Collaborative robots that physically interact with humans in an ergonomic and safe manner are essential to the future of industry. A common task across many industrial applications is robot-to-human handover, in which the location of object exchange is vital in cultivating a seamless interaction. Most prior work on computing these exchange locations aims to adjust human posture towards a better ergonomic state during a single handover. This procedure typically involves the robot estimating the human’s biomechanical properties, e.g. center of mass and base of support, before determining an optimal handover location according to some ergonomics assessment scale. In a similar vein, we compare two methodologies for object handover, whereby the handover location is computed to either "assist" or "stimulate" the human receiver. Unlike existing approaches, we posit that improvements in human posture can be derived by stimulating the receiver’s movement dynamics to facilitate posture variability, rather than constrain or stabilize it. To compare methodologies, we conduct a within-subjects study where participants perform 78 object handovers with a collaborative robot architecture. Our ndings indicate an improvement in ergonomics scores for the "stimulating" approach, hinting at the importance of productive inconvenience in long-term robot-to-human handover.
Statistical properties of fidelity in quantum tomography protocols in Hilbert spaces of different dimensions

A throughout study of statistical characteristics of fidelity in different protocols of quantum tomography is given. We consider protocols based on geometry of platonic solids and other polyhedrons with high degree of symmetry such as fullerene and its dual polyhedron. Characteristics of fidelity in different protocols are compared to the theoretical level of the minimum possible level of fidelity loss. Tomography of pure and mixed states in Hilbert spaces of different dimension is analyzed. Results of this work could be used for a better control of quantum gates and quantum states in quantum information technologies.
Improving Aspect Extraction based on Rules through Deep Syntax-Semantics Communication

Recent studies show integrating language re-001 sources which consist of lexical resources, 002 syntactic resources and semantic resources 003 can improve the performance of natural lan-004 guage processing (NLP) tasks. The existing 005 methods mostly perform simple integration 006 through concatenating these resources succes-007 sively, seldom consider complementary rela-008 tionship among them, such as the deep com-009 munication of syntactic and semantic relations 010 between words. To enhance deep syntax-011 semantics communication, this paper takes as-012 pect term extraction (ATE) task as an example 013 and explores four integration strategies of lan-014 guage resources. These strategies, based on 015 Answer Set Programming (ASP) rules, have 016 interpretability. Experiments on eight ATE 017 datasets show that our strategies achieve su-018 perior performance, demonstrating that they 019 are highly effective in integrating language re-020 sources. 021
IMPROVED Ti ii log(gf) VALUES AND ABUNDANCE DETERMINATIONS IN THE PHOTOSPHERES OF THE SUN AND METAL-POOR STAR HD 84937

Atomic transition probability measurements for 364 lines of Ti ii in the UV through near-IR are reported. Branching fractions from data recorded using a Fourier transform spectrometer (FTS) and a new echelle spectrometer are combined with published radiative lifetimes to determine these transition probabilities. The new results are in generally good agreement with previously reported FTS measurements. Use of the new echelle spectrometer, independent radiometric calibration methods, and independent data analysis routines enables a reduction of systematic errors and overall improvement in transition probability accuracy over previous measurements. The new Ti ii data are applied to high-resolution visible and UV spectra of the Sun and metal-poor star HD 84937 to derive new, more accurate Ti abundances. Lines covering a range of wavelength and excitation potential are used to search for non-LTE effects. The Ti abundances derived using Ti ii for these two stars match those derived using Ti i and support the relative Ti/Fe abundance ratio versus metallicity seen in previous studies.