Miracle-dz/UniFAR-FTUs huggingface dataset:
Usability Evaluation of a Robotic System for Cognitive Testing

This abstract presents a preliminary evaluation of the usability of a novel system for cognitive testing, which is based on the multimodal interfaces of the social robot “Pepper” and the IBM cloud AI “Watson”. Thirty-six participants experienced the system without assistance and filled the System Usability Scale questionnaire. Results show that the usability of the system is highly reliable.
A New Design for Alignment-Free Chaffed Cancelable Iris Key Binding Scheme

Iris has been found to be unique and consistent over time despite its random nature. Unprotected biometric (iris) template raises concerns in security and privacy, as numerous large-scale iris recognition projects have been deployed worldwide—for instance, susceptibility to attacks, cumbersome renewability, and cross-matching. Template protection schemes from biometric cryptosystems and cancelable biometrics are expected to restore the confidence in biometrics regarding data privacy, given the great advancement in recent years. However, a majority of the biometric template protection schemes have uncertainties in guaranteeing criteria such as unlinkability, irreversibility, and revocability, while maintaining significant performance. Fuzzy commitment, a theoretically secure biometric key binding scheme, is vulnerable due to the inherent dependency of the biometric features and its reliance on error correction code (ECC). In this paper, an alignment-free and cancelable iris key binding scheme without ECC is proposed. The proposed system protects the binary biometric data, i.e., IrisCodes, from security and privacy attacks through a strong and size varying non-invertible cancelable transform. The proposed scheme provides flexibility in system storage and authentication speed via controllable hashed code length. We also proposed a fast key regeneration without either re-enrollment or constant storage of seeds. The experimental results and security analysis show the validity of the proposed scheme.
A Compact Frequency Agile Patch Antenna With Agile Microstrip Feedline

A novel frequency reconfigurable patch antenna based on an agile feedline is presented. The proposed antenna is based on hexagonal-shaped patch antenna element. Frequency agility is realized with an agile microstrip feedline employing varactor diode. A hexagonal-shaped defected ground structure (DGS) is incorporated in the antenna’s ground plane to make the design compact. Reactive loading is used for further compactness. The achieved results indicate that the proposed antenna provides frequency agile characteristics with wide continuous tunability from 1.41 to 2.26 GHz and stable gain patterns. The antenna is fabricated and measured to verify the design. The measured and simulated results show good agreement. Moreover, the proposed antenna has a planar structure with compact size of 40 × 40 mm2, making it a promising candidate for present wireless communication antenna systems, particularly in handheld devices.
The Effects of Proactive Release Behaviors During Human-Robot Handovers

Most research on human-robot handovers focuses on how the robot should approach human receivers and notify them of the readiness to take an object; few studies have investigated the effects of different release behaviors. Not releasing an object when a person desires to take it breaks handover fluency and creates a bad handover experience. In this paper, we investigate the effects of different release behaviors. Specifically, we study the benefits of a proactive release, during which the robot actively detects a human grasp effort pattern. In a 36-participant user study11The study is ready to reproduce with a Baxter robot. The code and environment setup is available at https://github.com/umhan35/handover_moveit, results suggest proactive release is more efficient than rigid release (which only releases when the robot is fully stopped) and passive release (the robot detects pulling by checking if a threshold value is reached). Subjectively, the overall handover experience is improved: the proactive release is significantly better in terms of handover fluency and ease-of-taking.
TRANSMISSION ELECTRON MICROSCOPY TECHNIQUES APPLIED TO THE SOLID PHASE CHARACTERIZATION OF ENVIRONMENTAL SAMPLES

The characterization of environmental samples is not trivial due to the presence of many crystallographic phases, particles sizes and elemental composition in concentrations down to trace level. Amongst the available techniques for solid phase analyses and characterization, transmission electron microscopy (TEM) is unique on the investigation of both elemental and phase composition of samples down to nanometer and even atomic scale. However, TEM techniques are not to be used in exploratory studies of solid phases because just a very small part of the sample is investigated. It would be necessary previous bulk analyses of the sample. On the other hand, TEM is a very powerful technique to thoroughly characterize solid phases with high spatial and analytical resolutions. In this work high-resolution transmission imaging, electron diffraction, energy dispersive X-ray spectroscopy, and electron energy-loss spectroscopy are briefly described to show their potential on the characterization of environmental samples.
Arabic Semantic Similarity Approaches - Review

Semantic similarity between different texts is a challenging task. It measures the relation between texts, sentences and words to depict their degree of similarity or resemblance. Many of the Natural languages processing tasks and applications need measuring the text semantic similarity to achieve good results. Plagiarism detection, text entailment, text summarization, machine translation, and information extraction are among these applications. Therefore many Methods for measuring semantic similarity have been proposed for Arabic text which is reviewed and compared in this research.
Domain wall induced modulation of low field H-T phase diagram in patterned superconductor-ferromagnet stripes

We present a systematic study of the magnetic domain wall induced modulation of superconducting transition temperature (Tc) in Nb/Ni bilayer stripes. By varying the thickness of the Ni layer from 20 nm to 100 nm we have been able to measure the low field Tc-H phase diagram spanning the Neel domain wall and Bloch domain wall range of thicknesses. Micromagnetic simulations and magnetic force microscopy measurements confirmed a stronger out-of-plane stray field in the Bloch domain walls compared to the Neel walls. A suppression in Tc was observed in the magnetization reversal region of the Ni film, the magnitude of which followed linearly to the strength of the out-of-plane stray field due to the domain walls. The magnitude of the stray field was quantified by comparing the Tc of the suppressed region of H-Tc phase diagrams with the unaffected part of the H-Tc curve. With Bloch domain walls a change in Tc of more than 60 mK was observed which is much more compared to the earlier reports. We believe that the narrow stripe geometry of the bilayers and the transverse external field maximized the effect of the domain walls in the Ni layer on the overlying superconducting film, leading to a larger change in Tc. This observation may be useful for domain wall controlled switching devices in superconducting spintronics.
Design, Commissioning and Start-up of a New Hydrothermal Liquefaction Continuous Pilot Unit

Hydrothermal liquefaction (HTL) has been demonstrated to be an effective emerging technology for the conversion of various biomass slurries into valuable biofuels and bioproducts precursors. Many studies have been carried out in batch laboratory-scale apparatus, an effective technique to understand the conversion process applied on several wet materials, from algae to organic wastes and lignocellulosic streams. On the other hand, some examples of continuous system have been implemented and tested worldwide as first step for the industrial scale-up of the technology. This work focuses on the development from the design to the start-up and commissioning of a new continuous HTL unit, established in RE-CORD laboratories.
Dual Adaptive Adjustment for Customized Garment Pattern

We present a dual adaptive garment slice adjustment technique for automatic resizing of apparel products with variant body shapes, and this technique can quickly generate clothed characters. Our first contribution is to propose a novel fit evaluation method. When a 2D garment pattern and a 3D draped garment have the same triangle topology connection, we calculate the shape variable of each triangle and output heat map simultaneously. For sewing a pattern to a newly targeted human body, we propose a fully automatic adjustment method that conforms to the body structure and is composed of two stages. In the coarse auto adjustment (CAA) stage, we propose a method of controlling the size of a garment by the length of a bounding box in five parts of the human body. Then, the garment pattern is automatically adjusted using the measured dimension, by stretching or shrinking. In the fine auto adjustment (FAA) stage, boundary vertices control the shape in the adjustment process. For better matching with the body, the vertices of the garment pattern boundary are relocated with the calculated moving distance and moving direction. As demonstrated in the results, our method enables fully automatic adjustment, preserving the original pattern style of garments between characters with vastly body shapes. Compared with the state-of-art 2D editing method, our proposed approach leads to time saving, and it achieves realistic garment effect compared to auto fitting methods.
Face Recognition Using Local Binary Pattern and Nearest Neighbour Classification

Face recognition system has been widely applied in daily activities such as identity authentication and human-computer interaction. This study aims to implement a human face recognition system with local binary pattern (LBP) and nearest neighbour algorithms. LBP algorithm was used as a descriptor to extract face features. For the classifier, it is proposed to use simple but powerful nearest neighbour methods. This paper used three public face datasets to evaluate the performance of the system, i.e., AT& T, JAFFE and Yale datasets. Train/test split (TTS) cross-validation method was conducted in the experiment to achieve a robust result. The result of this study demonstrated that the proposed system achieved 93% of accuracy for the AT& T dataset and 82% of accuracy for the Yale dataset due to the complexity of these datasets. However, it was able to recognise faces in the JAFFE dataset with the highest recognition rate of 100%.
OpenEHR and General Data Protection Regulation: Evaluation of Principles and Requirements

Background Concerns about privacy and personal data protection resulted in reforms of the existing legislation in the European Union (EU). The General Data Protection Regulation (GDPR) aims to reform the existing directive on the topic of personal data protection of EU citizens with a strong emphasis on more control of the citizens over their data and in the establishment of rules for the processing of personal data. OpenEHR is a standard that embodies many principles of interoperable and secure software for electronic health records (EHRs) and has been advocated as the best approach for the development of hospital information systems. Objective This study aimed to understand to what extent the openEHR standard can help in the compliance of EHR systems to the GDPR requirements. Methods A list of requirements for an EHR to support GDPR compliance and also a list of the openEHR design principles were made. The requirements were categorized and compared with the principles by experts on openEHR and GDPR. Results A total of 50 GDPR requirements and 8 openEHR design principles were identified. The openEHR principles conformed to 30% (15/50) of GDPR requirements. All the openEHR principles were aligned with GDPR requirements. Conclusions This study showed that the openEHR principles conform well to GDPR, underlining the common wisdom that truly realizing security and privacy requires it to be built in from the start. By using an openEHR-based EHR, the institutions are closer to becoming compliant with GDPR while safeguarding the medical data.
Active topolectrical circuits

Significance Originally discovered in condensed-matter physics, topological protection has become a unifying paradigm for understanding robust localized wave propagation in electronic, optical, acoustic, and even geophysical systems. The excitation of topologically protected waves in passive matter typically requires external forcing in a specific frequency range. Here, we show both theoretically and experimentally that robust topological edge modes can be spontaneously self-excited in active systems made from internally powered subunits. Presenting different realizations of active nonlinear electronic circuits, we demonstrate the emergence of self-organized topological wave patterns, in close agreement with predictions from a generic mathematical model. More broadly, these results can provide guidance for designing autonomous active systems with topologically protected signaling and transmission properties. The transfer of topological concepts from the quantum world to classical mechanical and electronic systems has opened fundamentally different approaches to protected information transmission and wave guidance. A particularly promising emergent technology is based on recently discovered topolectrical circuits that achieve robust electric signal transduction by mimicking edge currents in quantum Hall systems. In parallel, modern active matter research has shown how autonomous units driven by internal energy reservoirs can spontaneously self-organize into collective coherent dynamics. Here, we unify key ideas from these two previously disparate fields to develop design principles for active topolectrical circuits (ATCs) that can self-excite topologically protected global signal patterns. Realizing autonomous active units through nonlinear Chua diode circuits, we theoretically predict and experimentally confirm the emergence of self-organized protected edge oscillations in one- and two-dimensional ATCs. The close agreement between theory, simulations, and experiments implies that nonlinear ATCs provide a robust and versatile platform for developing high-dimensional autonomous electrical circuits with topologically protected functionalities.
Effective Clustering Analysis Based on New Designed CVI and Improved Clustering Algorithms

Due to different settings of the parameters and random selection of initial clustering centers, the traditional K-means algorithm is not stable. Clustering validity index (CVI) is an important method for evaluating the effect of clustering results generated by clustering algorithms. However, many of the existing CVIs suffer from instability, narrow range of applications and cannot properly process datasets with non-spherical distribution and datasets with a large number of overlapping points. Aiming at these problems, the traditional K-means algorithm is firstly improved by utilizing the dynamic average distance to find the initial clustering centers rather than selecting them randomly. Then, based on the idea of dynamic average distance, a new clustering validity index, DCVI, is proposed. The new DCVI is able to deal with many kinds of datasets includes non-convex datasets and datasets with a large number of overlapping points. Thirdly, by integrating the improved K-means algorithm with the new DCVI, a new algorithm (KVOA) is designed to optimize and determine the optimal clustering number (Kopt) for a wide range of datasets. The experimental results on testing several datasets have demonstrated that the improved K-means algorithm is more accurately and stably than the traditional ones. Meanwhile, the new DCVI is compared with six commonly used CVIs. The experimental results show that our new DCVI is more accurately and stably than the other CVIs.
A 3D LiDAR Data-Based Dedicated Road Boundary Detection Algorithm for Autonomous Vehicles

Effectively detecting road boundaries in real time is critical to the applications of autonomous vehicles, such as vehicle localization, path planning, and environmental understanding. To precisely extract the irregular road boundaries or those blocked by obstructions on the road from the 3D LiDAR data, a dedicated algorithm consisting of four steps is proposed in this paper. The steps are as follows. First, the 3D LiDAR data is pre-processed, employing the vehicle position and attitude information, and many noise points are deleted. Second, the ground points are quickly separated from the pre-processed point cloud data to reduce the disturbance from the obstacles on the road; this greatly decreases the size of the points cloud to be processed. Third, the candidate points of the road boundaries are searched along the predicted trajectory of the autonomous vehicle and filtered using the unique features of the boundary points. Last, a spline fit model is applied to smoothen the road boundaries. An experiment to test the performance of the proposed algorithm was conducted on the “Xinda” autonomous vehicle under various road scenarios. The experimental results show that the average accuracy of the proposed algorithm exceeds 93%, and its average processing time is approximately 36.5 ms/frame, which outperforms most of the state-of-the-art methods. This indicates that the proposed algorithm can robustly extract the road boundary in real time even if there are many obstacles on the road. This algorithm has been tested on “Xinda” autonomous vehicle for over 1000 kilometers, and its performance was always stable.
Learning Affective Video Features for Facial Expression Recognition via Hybrid Deep Learning

One key challenging issues of facial expression recognition (FER) in video sequences is to extract discriminative spatiotemporal video features from facial expression images in video sequences. In this paper, we propose a new method of FER in video sequences via a hybrid deep learning model. The proposed method first employs two individual deep convolutional neural networks (CNNs), including a spatial CNN processing static facial images and a temporal CN network processing optical flow images, to separately learn high-level spatial and temporal features on the divided video segments. These two CNNs are fine-tuned on target video facial expression datasets from a pre-trained CNN model. Then, the obtained segment-level spatial and temporal features are integrated into a deep fusion network built with a deep belief network (DBN) model. This deep fusion network is used to jointly learn discriminative spatiotemporal features. Finally, an average pooling is performed on the learned DBN segment-level features in a video sequence, to produce a fixed-length global video feature representation. Based on the global video feature representations, a linear support vector machine (SVM) is employed for facial expression classification tasks. The extensive experiments on three public video-based facial expression datasets, i.e., BAUM-1s, RML, and MMI, show the effectiveness of our proposed method, outperforming the state-of-the-arts.
A NOMA-Enhanced Reconfigurable Access Scheme With Device Pairing for M2M Networks

This paper aims to address the distinct requirements of machine-to-machine networks, particularly heterogeneity and massive transmissions. To this end, a reconfigurable medium access control with the ability to choose a proper access scheme with the optimal configuration for devices based on the network status is proposed. In this scheme, in each frame, a separate time duration is allocated for each of the non-orthogonal multiple access (NOMA)-based, orthogonal multiple access (OMA)-based, and random access-based segments, where the length of each segment can be optimized. To solve this optimization problem, an iterative algorithm consisting of two sub-problems is proposed. The first sub-problem deals with selecting devices for the NOMA-/OMA-based transmissions, while the second one optimizes the parameter of the random access scheme. To show the efficacy of the proposed scheme, the results are compared with the reconfigurable scheme which does not support NOMA. The results demonstrate that by using a proper device pairing scheme for the NOMA-based transmissions, the proposed reconfigurable scheme achieves better performance when NOMA is adopted.
Bayesian Coalition Formation Game for Virtual 5G Core Network Functions

The network function virtualization (NFV) enables the virtual network functions (VNFs) to run as software components upon a virtualization system hosted in a cloud. Recently, due to its high flexibility and elasticity in terms of network services and functions deploying, NFV has been considered as one of the key enabling techniques of the fifth-generation (5G) wireless systems. Moreover, the creation of the core network functions is the key to the implementation of 5G. Currently, there are extensive studies researching into how to deploy cooperatively VNFs over a federated cloud for multiple cloud providers (CPs) to create the 5G Core. These studies have a critical assumption that each CP participating in the cooperation is fully informed of all the information about other CPs. However, the assumption may not hold in practice. Since CPs adopt diverse virtualization technologies (e.g., XEN, KVM, or containers), the cost to create an instance of one VNF for each CP may be different. Moreover, the cost information is private and will not be shared with each other. Consequently, it is difficult for each CP to determine whether to cooperate with others and the way of cooperation. Therefore, in this paper, we utilize the Bayesian coalition formation game (BCFG) to tackle this challenging situation with the unknown information about CPs and formulate the optimal coalition that can deploy cooperatively each VNFs of 5G Core. Specifically, we address the optimal number of VNF instances to instantiate, the number of virtual resources for each instance, and the placement decision in a specific CP over a federated cloud. We propose a BCFG-based non-myopic and full negotiation approach for the creation of the virtual 5G Core network functions. Simulation results demonstrate the comprehensive performance of the proposed method in terms of convergence properties and the profits that each participating CP can obtain.
Scenario Analysis-Based Decision and Coordination in Supply Chain Management with Production and Transportation Scheduling

The production and rail transportation coordinating problem aims to determine a pre-operational timetable for a set of orders. The orders need to be manufactured on a dedicated production line and be delivered to their destination by the pre-planned trains considering traveling routes and time. The connecting trains are an important and even unique chance for orders to reach their final destinations, and earliness and tardiness penalties in the connection and delivery process are closely related to the symmetry and harmony between production procedure decision and train-allocated choice. A scenario analysis method is adopted to reveal the relationships between production collection connecting time (PCCT) and production collection waiting time (PCWT) in the production process. In the delivery process, the relationships between production delivery connecting time (PDCT) and production delivery waiting time (PDWT) are mainly considered. An integrated scheduling coordination model is established to maximize the production connecting time (PCT) and production delivery time (PDT). The GA genetic algorithm is employed to solve this problem. The numerical results show that the coordinated schedule in our method can significantly reduce the number of missed connections when compared with considering delivery timeliness only through a delivery time window. Additionally, it is revealed that the quantitative performances of the delivery timeliness improved by the connecting quality (PCT) are much better than those from the view of the delivery time window (PDT).