Miracle-dz/UniFAR-FTUs huggingface dataset:
A Benchmark Toolkit for Collaborative Human-Robot Interaction

Novel human-robot collaboration (HRC) methods need careful validation. This is often achieved with user studies in laboratory environments, which mostly rely on highly individual, complex prototype setups in early design stages. Lately, the lack of replicability imposed by such experiments has vitally been discussed. In this paper, we contribute a benchmark toolkit to compose scalable, synthetic tasks as a unified basis for future efforts towards more replicable research. To this end, we pro-pose modular task boards which cover different domains and HRC scenarios. The design of benchmark tasks with these task boards is supported by a software application which generates structural task models and materials for benchmark problem reproduction by 3D printing. Our experiments show that these tasks can be carried out robustly by robots, hence preventing unintended robot failure and providing a controllable, reliably reproducible setting for user studies.
Alzheimer's Disease Detection via a Surrogate Brain Age Prediction Task using 3D Convolutional Neural Networks

Structural magnetic resonance imaging (MRI) studies demonstrated that Alzheimer's Disease (AD) causes not only local but also whole-brain level neural degenerative changes. To assess such changes, convolutional neural networks (CNN) are a popular approach as they are very capable automated feature extractors. In this work, due to the lack of segmentation that highlights brain degenerative changes, CNN-based brain age pre-diction is used as a surrogate task for training a feature extractor. Using the 3D T1-weighted MRI data of cognitive normal (CN) subjects from the OASIS-3 dataset, lightweight 3D CNN-based models are trained to predict brain age. The extracted features are then used in the binary classification of CN vs AD patients from their brain MRI scans. To established a baseline, we used support vector machines and random forest classifier as base classifiers. Our results suggest that the 3D MRI driven CNN brain age prediction surrogate task approach can learn AD-relevant features with high discriminative power without a complicated pipeline of preprocessing or data augmentation. Highlighting the novelty of the approach: the train-test-split is carefully performed on subject-level to avoid data leakage; the spatial information of 3D volumetric data is fully utilised; the robustness is proven by not using complicated data preprocessing and augmentation techniques.
GEAE: Gated Enhanced Autoencoder based Feature Extraction and Clustering for Customer Segmentation

Customer segmentation is a core issue in the customer relationship management community. It provides an important reference for companies to understand customers' needs and develop accurate marketing programs by dividing customers with similar needs into specific categories. With the advent of the era of big data, the recorded data of customer characteristics become more diversified, and the data dimension presents a trend of sharp growth. The relationships among data have become more complicated, and both manual statistical analysis and traditional clustering algorithms have encountered bottlenecks. A kind of work that uses autoencoder to reduce dimensions and then conducts clustering research provides a good solution to this problem. Autoencoder can effectively capture nonlinear and complex data features by stacking hidden layers with activation functions. However, the uncontrollability of the encoding-decoding process in autoencoders is neglected in the existing research on autoencoders. To solve this problem, a gated enhanced autoencoder(GEAE) is proposed in this paper. The gating block connection between encoder and decoder is established to make the encoding process guide the decoding process and reduce the uncontrollability of the encoding-decoding process. We verify the validity of the proposed method on a real data set for customer segmentation. GEAE obviously outperforms AE in terms of both reconstruction performance and the effect of downstream clustering tasks. In addition, this research also verifies the importance of selecting the appropriate position of the gating block in GEAE through ablation experiments.
Super-Twisting Sliding Mode Control with Adaptive Gain of Quadrotor with Rigid Manipulator

This paper presents the control of an aerial manipulator composed of a quadrotor and a manipulator with two degrees of freedom (2 DOF). There is a strong physical coupling between the dynamics of the UAV and the manipulator arm. This coupling along with external perturbations (e.g. wind gusts), considerably affects the stability of the drone’s motion in flight which, consequently, affects the desired accuracy of the end effector with respect to its final task. The solution presented here is based on super-twisting sliding mode control with adaptive gain. It is a continuation of previous study on sliding mode control with adaptive gain [1]. The effectiveness of the controller and its robustness against perturbations are verified and analyzed using numerical simulation.
Modeling Customer Satisfaction based on Kano Model from Online Reviews: Focused on Deep Learning Natural Language Processing

Online reviews are data written voluntarily by customers and have become relatively easy to collect in recent years. Because it can be easily collected, online reviews can be used as alternative data to understand customer satisfaction and many studies have attempted customer satisfaction modeling by converting online reviews, which are unstructured data, into structured data. Hence, this study presented an improved framework for conducting customer satisfaction modeling from online reviews and mixed unsupervised learning and supervised learning methods. Also, this study proposed a methodology using deep learning-based natural language processing techniques to supplement the methodological limitations of existing studies and to secure structured data for customer satisfaction modeling in the absence of labeled data. The study is expected to demonstrate the necessity of a detailed product improvement strategy through modeling according to the improvement of processing techniques for review text in customer satisfaction modeling research and to provide efficient product development/improvement guidelines to promote customer satisfaction.
Robots for Connection: A Co-Design Study with Adolescents

Adolescents isolated at home during the COVID19 pandemic lockdown are more likely to feel lonely and in need of social connection. Social robots may provide a much needed social interaction without the risk of contracting an infection. In this paper, we detail our co-design process used to engage adolescents in the design of a social robot prototype intended to broadly support their mental health. Data gathered from our four week design study of nine remote sessions and interviews with 16 adolescents suggested the following design requirements for a home robot: (1) be able to enact a set of roles including a coach, companion, and confidant; (2) amplify human-to-human connection by supporting peer relationships; (3) account for data privacy and device ownership. Design materials are available in open-access, contributing to best practices for the field of Human-Robot Interaction.
Cooperation in two-dimensional mixed-games

Evolutionary game theory is a common framework to study the evolution of cooperation, where it is usually assumed that the same game is played in all interactions. Here, we investigate a model where the game that is played by two individuals is uniformly drawn from a sample of two different games. Using the master equation approach we show that the random mixture of two games is equivalent to play the average game when (i) the strategies are statistically independent of the game distribution and (ii) the transition rates are linear functions of the payoffs. We also use Monte-Carlo simulations in a two-dimensional lattice and mean-field techniques to investigate the scenario when the two above conditions do not hold. We find that even outside of such conditions, several quantities characterizing the mixed-games are still the same as the ones obtained in the average game when the two games are not very different.
Effect of End-to-end Delay on Lifetime and Average Bit Error Rate in Directional Antenna Based Wireless Sensor Networks

This paper evaluates the effect of end-to-end delay on lifetime and the average bit error rate for wireless sensor networks where each sensor node sends its data to sink via directional antennas. Using Mixed Integer Programming, lifetime is optimized for circular network topologies with different numbers of nodes. Using both directional and omnidirectional antennas, different end-to-end delay restrictions are applied to the same sensor network to examine the effect on lifetime and average bit error rate. When delay restriction is relaxed, an increase in lifetime and a decrease in average bit error rate are observed for both antennas. In this case, the directional antenna benefits more from relaxing the delay restriction. In the end, an optimal end-to-end delay was determined which provides sufficient lifetime and bit error rate while taking into account data freshness.
Evaluation of Point of Subject Equality Using Constant Method in Pseudo Force Sensation by Pressure Stimulation to the Palm*

A psycho-physical experiment using constant method was conducted to investigate the characteristics of pseudo force sensation caused by pressurization to a palm. Kinesthetic force stimulus and tactile pressure stimulus were given to a subject at the same time, and their effect on force perception was investigated. Previous studies have reported two contradicting results regarding whether the strength of pseudo force sensation depends on the pressure or on the total amount of force. This paper further investigated this aspect using three types of indenters which differ in shape and width. The results suggested that the pseudo force sensation does not depend on total force. This, however, does not simply mean that the sensation depends on the pressure and the need for further investigations remains. The results found significant differences among subjects but also suggested that the narrower indenter with an integrated body is less susceptible to individual differences among subjects.
Capturing Local and Global Spatial-Temporal Correlations of Spatial-Temporal Graph Data for Traffic Flow Prediction

Traffic flow prediction is a challenging task due to complex spatial-temporal correlations. Most existing methods leverage graph convolutional network (GCN) to capture spatial correlations. However, GCN has limited ability in mining global spatial correlations. Multi-layer GCN for aggregating multi-order neighbor information will result in high-degree nodes being prone to over-smoothing. To this end, we develop a graph convolutional recurrent attention network (GCRAN) for traffic flow prediction. Specifically, we take the advantage of Gated Recurrent Units (GRU) and Attention to explore local and global temporal correlations. Moreover, we design a novel local context aware spatial attention to extract local and global spatial correlations simultaneously. Experiments on two public real-world traffic datasets demonstrate that GCRAN outperform state-of-the-art baselines.
Disc clearing of young stellar objects: evidence for fast inside-out dispersal

The time-scale over which and the modality by which young stellar objects (YSOs) disperse their circumstellar discs dramatically influences the eventual formation and evolution of planetary systems. By means of extensive radiative transfer (RT) modelling, we have developed a new set of diagnostic diagrams in the infrared colour-colour plane (K-[24] vs. K-[8]), to aid with the classification of the evolutionary stage of YSOs from photometric observations. Our diagrams allow the differentiation of sources with unevolved (primordial) discs from those evolving according to different clearing scenarios (e.g. homologous depletion vs. inside-out dispersal), as well as from sources that have already lost their disc. Classification of over 1500 sources in 15 nearby star-forming regions reveals that approximately 39 % of the sources lie in the primordial disc region, whereas between 31 % and 32 % disperse from the inside-out and up to 22 % of the sources have already lost their disc. Less than 2 % of the objects in our sample lie in the homogeneous draining regime. Time-scales for the transition phase are estimated to be typically a few 10^5 years independent of stellar mass. Therefore, regardless of spectral type, we conclude that currently available infrared photometric surveys point to fast (of order 10 % of the global disc lifetime) inside-out clearing as the preferred mode of disc dispersal.
Personalized Learning Using Kernel Methods and Random Fourier Features

Distributed learning algorithms have become an increasingly popular research topic due to the increasing proliferation of IoT devices and sensor networks with edge processing capabilities. This paper focuses on a distributed learning algorithm, Personalized Learning. In Personalized Learning, a population of $N$ edge devices learns its similarities while preserving its learnings individually. Personalized Learning consists of a two-step iterative approach: $N$ edge processors are learning for each $L$ “canonical” model, and the characteristics of canonical models are estimated based on the learned edges. In earlier works, linear and logistic regression models were used to estimate characteristics in the first step. This work extends the previous work on the framework for personalized learning using non-linear data. This research improves the computational speed and performance of the current framework using Kernel Methods by Random Fourier Features for learning the first layer and compares these learning methods with linear models in simulation studies.
Efficient Flow Scheduling for Industrial Time-Sensitive Networking: A Divisibility Theory-Based Method

As an emerging communication technology, time-sensitive networking (TSN) promises the real time and deterministic interaction of massive data in Industrial Internet of Things. However, it is challenging to schedule the time-sensitive flows timely and superiorly through the mechanism analysis for current TSN scheduling models, especially in complex industrial scenarios. In this article, we propose an analysis approach of flow sequences based on divisibility theory to characterize the flow conflicts and dependencies, which derives the scheduling flexibility based on flow position diversity (PD) and the equivalent flow judgment conditions for slot occupancy. Integrating the abovementioned derivation, a parallel computing framework with the generalized slot length is established to lower the scheduling complexity. Within each computing unit, an incremental scheduling algorithm with the flow judgment conditions and PD-based search boundary is proposed. It reduces the scheduling complexity further while maintaining load balance for the mixed transmission of periodic and aperiodic flows. To achieve the optimality of runtime and load balance, two PD-based flow sorting strategies are designed, respectively. The evaluation results show that compared with the existing works, the runtime efficiency of scheduling at scale is increased by at least 1500 times in complex traffic scenarios while the load balance on the network links is also improved.
Automated Vulnerable Codes Mutation through Deep Learning for Variability Detection

At present, there are many studies on the automatic generation or mutation of common code dataset, but the research on the mass generation of vulnerable code dataset has received little public attention. Wanting to generate more vulnerable code seems counterproductive, but it is very important to vulnerability detection technology. It can be used to discover the blind spots of vulnerability detection tools through fuzzing testing technology. In particular, the use of machine learning and deep learning techniques for vulnerability detection has highly dataset imbalance problem due to the lack of vulnerable codes, which seriously affects the performance of the vulnerability detection model. In this paper, we propose a new vulnerable code mutation technique called Vuls-Mutation. Based on a generative Sequence-to-Sequence model, our system automatically and continuously mutate to generate new vulnerable code, by changing the control flow or data flow of the potentially tainted data in the existing vulnerable code. Experiments show that the grammatical correctness rate of the mutated code is about 71 % and the true positive rate of the mutated code based on the correct grammar is about 93%. We add this new set of mutation programs to train deep learning vulnerability detection models, and the results show that all indicators are better than the baseline method.
A Hybrid Approach for Interpretable Game Performance Prediction in Basketball

Sports Data Analytics is an emerging field involving analytical tools to help coaches design practical strategies for athletic training and winning games. In this paper, we developed Decision Tree (DT) based models for analyzing the sleep and recovery data, training statistics and cognitive state of athletes in a Women's Division-I basketball team for game score prediction. We develop a hybrid approach that employs classification/regression trees and random forests to predict the weighted game score in conjunction with factor analysis. These factor weights are explained using a consensus score derived from a small collection of decision trees constructed during the prediction. These decision trees on further inputs from coaches can lead to a robust mechanism for interpreting model predictions. Our athlete�s data consisted of 2800 records, with 37 attributes obtained from 16 athletes over 25 weeks from strength coaches, sleep and recovery information from wrist-worn devices, and perception questionnaires. Our hybrid approach first performs factor analysis to compactly characterize the athlete's data in terms of 7 latent factors. It then uses these factors to build a collection of decision trees for predicting and interpreting the game score. The predictions from our approach have an MSE of 0.013 and an R2 of 0.971, which is better than classical multilinear regression (MSE 0.053, R2 0.689) and DT (MSE 0.036, R2 0.798) based approaches.
Bringing Big Data into Media: A Decision-Making Model for Targeting Digital News Content

The purpose of this study is to investigate how big data analytics technology affects decision-making in the media industry, with a focus on digital newspapers. To achieve this goal, we propose a decision-making model to identify the relationship between audiences and news topics using big data analysis and classification, intending to help news practitioners optimize their marketing strategies. To evaluate our model, we conducted a case study using a Swiss local newspaper. Preliminary results indicate that audience reading time and volume have been significantly increased after implementing the decision that based on the model’s analysis. The study also provides some guidelines for editors and journalists to target their digital news content.
THE Arp 220 MERGER ON kpc SCALES

For the first time, we study the eastern nucleus in greater detail and search for the more extended emission in the molecular gas in different CO line transitions of the famous ultraluminous infrared galaxy (ULIRG) Arp 220. Furthermore, we present a model of the merger in Arp 220 on large scales with the help of the CO data and an optical and near-infrared composite Hubble Space Telescope image of the prototypical ULIRG. Using the Plateau de Bure interferometer (PdBI), we obtained CO (2–1) and (1–0) data at wavelengths of 1 and 3 mm in 1994, 1996, 1997, and 2006 at different beam sizes and spatial resolutions. The simulations of the merger in Arp 220 were performed with the Identikit modeling tool. The model parameters that describe the galaxy merger best give a mass ratio of 1:2 and result in a merger of ∼6 × 108 yr. The low-resolution CO (1–0) PdBI observations suggest that there are indications for emission ∼10″ toward the south, as well as to the north and to the west of the two nuclei.
WMAP OBSERVATIONS OF PLANCK ESZ CLUSTERS

We examine the Sunyaev–Zeldovich (SZ) effect in the seven year Wilkinson Microwave Anisotropy Probe (WMAP) data by cross-correlating it with the Planck Early-release Sunyaev–Zeldovich catalog. Our analysis proceeds in two parts. We first perform a stacking analysis in which the filtered WMAP data are averaged at the locations of the 175 Planck clusters. We then perform a regression analysis to compare the mean amplitude of the SZ signal, Y500, in the WMAP data to the corresponding amplitude in the Planck data. The aggregate Planck clusters are detected in the seven year WMAP data with a signal-to-noise ratio of 16.3. In the regression analysis, we find that the SZ amplitude measurements agree to better than 25%: a = 1.23 ± 0.18 for the fit .
Learning Human Actions Semantics in Virtual Reality for a Better Human-Robot Collaboration

The advent of collaborative robots has revolutionised the work concept for Small Medium Enterprises (SME), introducing quick line configuration changes and allowing a human operator to work together with a robot. Several problems are still present regarding the need to reprogram the robotic collaborator. This paper exploits the concept of Virtual Reality to instruct the robotic co-worker, allowing a non-skilled operator to teach a task effortlessly and straightforwardly. Each Skill composing the task is classified and characterised through its pre and postconditions to let the robot stop and notify the operator in case of inability to complete the scheduled action. Moreover, the system allows the operator to interact with the robot and be proactive during the task execution.
On the Impact of Multimodal and Multisensor Biometrics in Smart Factories

Smart factories are fostered by integrating intelligent systems and ICT technologies. The role they play is crucial in the spread of Industry 4.0 and the economic growth of developed countries. Smart factories can be empowered by using several sensors aimed at making them more and more “smart. ” Unfortunately, work accidents are still very common resulting in human losses and permanent injuries. This makes it urgent and key to implementing security and safety measures also in the context of a smart factory. In this article, a novel framework for supporting smart devices in a smart factory, using multiple sensors to monitor different biometric features, both physical and behavioral is proposed. Thanks to the fusion of several biometric traits with the support of machine learning technologies working together with different kinds of sensors, it is possible to guarantee three fundamental aspects within the interaction between an operator of a smart device and the device itself: continuous authentication (i.e., continuous face recognition), drowsiness detection, and liveness detection. With the application of the proposed framework, it is possible to significantly improve the safety of operators avoiding fatal accidents for them. Experiments made using COTS-hardware showed that the authors’ idea is easy to implement in a large-scale smart factory and further improves the spread of Industry 4.0.