Miracle-dz/UniFAR-FTUs huggingface dataset:
Question-Answering System with Linguistic Terms over RDF Knowledge Graphs

Resource Description Framework (RDF) is an important way of representing data on the Web. Although RDF is a data format suitable for publishing individual pieces of information together with relations between them, it represents a challenging format for answering questions. Thus, a system and a user interface that are easy and intuitive for users to access and operate on RDF data are of significant importance.In this paper, we introduce a Question-Answering (QA) system that allows users to ask questions in English. The uniqueness of this system is its ability to answer questions containing linguistic terms, i.e., concepts such as SMALL, LARGE, or TALL. Those concepts are defined via membership functions drawn by users using a dedicated software designed for entering ‘shapes’ of these functions. The system is built based on an analogical problem solving approach, and is suitable for providing users with comprehensive answers. We demonstrate the capability of the proposed QA system by answering questions asked over two RDF stores: DBpedia and Wikidata.
Standards for Cloud Risk Assessments - What's Missing?

Conducting a risk assessment (RA) for cloud computing platforms presents new challenges in the space of Information Security Management Systems (ISMS). ISO 27001 RA methods have been used for the Alcohol Monitoring Systems (AMS) ISMS across a portfolio of products and services. Scaling these localized techniques to national and international cloud security standards shows that a ‘one size fits all’ risk assessment approach does not exist in the industry today. The context and methods for conducting cloud risk assessment are examined across representative national and international standards and guidelines. Recommendations for standardizing the RA methods for cloud computing are suggested based on industry best practices.
ADADRIFT: An Adaptive Learning Technique for Long-history Stream-based Recommender Systems

Adaptive recommender systems are increasingly showing their importance as profiling is a dynamic problem. Their goal is to update recommendation models as new interactions take place, thus swiftly adapting to drifts in the user’s behavior and desires, and item’s audience. However, existing recommendation algorithms usually do not perform well during drifts, as they take long to adapt to changes, or these updates are suboptimal since they account for all profiles’ preferences equally, which is often untrue as each individual and its changes are unique. In this paper, we propose the ADADRIFT algorithm to deal with user and item-based drifts in adaptive recommender systems using personalized learning rates based on profile statistics. The experiments using stream-based recommender systems (ISGD and BRISMF) across four different datasets show that ADADRIFT surpasses ADADELTA with significant improvements in recommendation rates. The best results appear when the data streams have a long history of the users’ or items’ interactions and drifts become noticeable. The experimentation in this work highlight the importance of handling drifts in recommender systems.
Analysis of Assistant Reactive Shielding Coil for Electric Vehicle Wireless Charging System

To realize electromagnetic safety of wireless power transmission (WPT) system for electric vehicle (EV), the shielding methods should be analyzed. This paper proposes a method of using assistant reactive shielding coils to cancel the electromagnetic field (EMF) outside the working area of WPT system. By tuning compensation capacitance of shielding coil automatically, we design 3 cases with different location and shape of coils to achieve reactive shielding. By comparison and simulation, placing ring shielding coils above receiving (Rx) pad and under transmitting (Tx) pad is the best way to suppress EMF of WPT system's non-working area. The shielding coils' radius is slightly bigger than coupling coils' outer radius. This paper also provides a new method to realize WPT shielding system's automatic adjustment for resonance. Besides, assistant reactive shielding coils' cost is lower than traditional aluminum plate.
A Novel One-stage Distributed Parallel Embedding for Virtualized Network Environment

Network virtualization recognized as an enabling technology for the forthcoming networks is utterly popular. One of the main challenges of network virtualization is called the virtual network embedding problem. Virtual network embedding (VNE) aims to allocate a set of virtual machines onto a set of interconnected physical hardware in the cloud computing environment. Traditional exact solutions, considered as a time-consuming process to achieve a global optimal solution, have been proofed to be $\mathcal{N}\mathcal{P}$-hard. On the other hand, some existing heuristic solutions tend to decouple VNE problems into two stages: virtual node mapping (VNoM) and virtual link mapping (VLiM). Undoubtedly, these kinds of decomposition would result in low acceptance ratio and inefficient substrate resource utilization. In this paper, we propose a distributed parallel Genetic Algorithm combined with graph theory for solving VNE in one-stage. Our proposed algorithm achieves better performance than previous baseline solutions while meeting the stringent time requirements for online VNE problems.
Improving ECG Classification Interpretability using Saliency Maps

Cardiovascular disease is a large worldwide healthcare issue; symptoms often present suddenly with minimal warning. The electrocardiogram (ECG) is a fast, simple and reliable method of evaluating the health of the heart, by measuring electrical activity recorded through electrodes placed on the skin. ECGs often need to be analyzed by a cardiologist, taking time which could be spent on improving patient care and outcomes.Because of this, automatic ECG classification systems using machine learning have been proposed, which can learn complex interactions between ECG features and use this to detect abnormalities. However, algorithms built for this purpose often fail to generalize well to unseen data, reporting initially impressive results which drop dramatically when applied to new environments. Additionally, machine learning algorithms suffer a ‘black-box’ issue, in which it is difficult to determine how a decision has been made. This is vital for applications in healthcare, as clinicians need to be able to verify the process of evaluation in order to trust the algorithm.This paper proposes a method for visualizing model decisions across each class in the MIT-BIH arrhythmia dataset, using adapted saliency maps averaged across complete classes to determine what patterns are being learned. We do this by building two algorithms based on state-of-the-art models. This paper highlights how these maps can be used to find problems in the model which could be affecting generalizability and model performance. Comparing saliency maps across complete classes gives an overall impression of confounding variables or other biases in the model, unlike what would be highlighted when comparing saliency maps on an ECG-by-ECG basis.
Augmented Reality for Pack Optimization using Video and Depth Data

Augmented Reality (AR) can be used in intra-logistics to optimize packing processes towards shipping less unused space by calculating and visualizing efficient pack schemas. This work describes the prototype of such a system using a Kinect v2. Based on the delivered RGB-D stream detection and tracking algorithm applied particle filters, tracks boxes and articles. Further, the tracking approach can check if an article is placed correctly in the box. A qualitative assessment of the prototype in a warehouse revealed that such a system only useful for unexperienced packers.
ANN-inversion Based Fractional-Order Sliding Control for the Robot

To improve the control performance of the industrial robot, an ANN-inversion based fractional order sliding mode control(FOSMC) scheme is proposed. Firstly, the BP neural network is used for the inversion of the industrial robot, and approximate decoupling and linearization of the industrial robot is got. Secondly, the composite pseudo linear system, w hich is composed of the ANN-Inversion system and the con-trolled industrial robot, is equivalent to a linear system with disturbance in view of the industrial robot uncer-tainties and the BP neural network's approximation error. Then, the fractional-order sliding control(FOS-MC) scheme is proposed based on the SM Ctheory and fractional calculus for the linear system with disturb-ance, and the stability analysis is given. Finally, case study is fulfilled for a two -DOF robot under different conditions, and results show the effectiveness of the proposed control scheme.
Improving Aggregated Load Forecasting Using Evidence Accumulation k-Shape Clustering

Aggregated load forecasting provides a basis for load aggregators to take part in the power market. By separating time series into several groups according to shape characteristic, k-Shape clustering-based approach is an effective way to implement aggregated forecasting. In this paper, we aim at further improving its performance on both probabilistic and deterministic aggregated forecasts by using ensemble technique. By transforming the partitions of load profiles into the similarity matrix, an evidence accumulation k-Shape clustering method is proposed to establish the hierarchical similarity structure of customers. Then, through varying the number of clusters, multiple probabilistic or deterministic aggregated forecast results are obtained. Determination of the optimal weights for combining the results is formulated as linear programming (LP) problems, with the objective of minimizing the pinball loss or mean absolute percent error (MAPE) respectively. Case study on the open dataset demonstrates the superiority of the proposed method.
Studies of Different Swarm Modes for the MNPs Under the Rotating Magnetic Field

The principal constraint in the application of the microrobots in drug delivery is swarm control. The rotating magnetic field has shown the potential of capturing and controlling the swarm of magnetic nanoparticle-based microrobots. Despite various experimental studies to capture the swarm of the magnetic nanoparticle-based microrobot in a rotating magnetic field, a simulation platform for the swarm of aggregated magnetic nanoparticles (MNPs) has not been introduced. This study proposes a simulation platform to study the swarm of aggregated magnetic nanoparticles in a rotating field. An experimental setup was developed to investigate the different swarm modes in a rotating magnetic field, and the results show an agreement between the experimental and simulation results for the micro- and nanoparticles. The effects of the environmental parameters (initial dispersion of nanoparticles), process parameters (magnetic field intensity, frequency, actuation time), and geometrical parameters (particle diameter) were studied to describe the swarm behavior in a rotating magnetic field. These studies revealed the role of each parameter in creating the swarm and showed how the size of aggregates can be controlled. The presented approach can be used to design the magnetic nanoparticle-based microrobots effectively.
Research of a Resistive-Type SFCL for Protection of a Power Electronic Transformer

The stability of a power electronic transformer (PET) under short-circuit faults is a crucial issue. In this paper, the output-terminal fault of the PET is considered, and a resistive-type superconducting fault current limiter (SFCL) is applied in the PET for guarding against the fault surge. The configuration structure of the PET integrating with the resistive-type SFCL is presented, and the SFCL's potential influence on the PET's fault current is theoretically probed. Using the MATLAB platform, the digital model of the PET-SFCL is built, and the three-phase fault scenario is simulated. The simulation results reveal that the resistive-type SFCL enables to very usefully refrain the three-phase fault currents of the PET. The fault current level can be stably suppressed lower than the safety margin, and the PET's stability during the fault is availably increased.
Evaluating Remote Virtual Hands Models on Social Presence in Hand-based 3D Remote Collaboration

This study investigates the effects of a virtual hand representation on the user experience including social presence during hand-based 3D remote collaboration. Although a remote hand appearance is a critical parts of a hand-based telepresence, it has been rarely studied in comparison to studies on the self-embodiment of virtual hands in a 3D environment. Thus, we conducted a user study comparing the three virtual hands models (Skeleton, Low Polygon and Realistic) while performing a remote collaborative task based on the American Sign Language (ASL) using both Augmented Reality (AR) and Virtual Reality (VR) environments. We found that the realistic type was perceived as the most sense of being together, human-like, and trustable representation. The low polygon model could also convey a clear sign and moderate level of social presence. Although the system was configured asymmetrically in AR and VR, little difference in perception was found except for the participant’s mental load and message understanding. We then discuss the results and suggest design implications for future hand-based 3D telepresence systems.
The Fixed Route Electric Vehicle Charging Problem with nonlinear energy management and variable vehicle speed

The problem of an individual who wants to plan a long route in an electric vehicle where charging decisions are needed can be modeled as an instance of the Fixed Route Electric Vehicle Charging Problem (FRVCP). We developed a mixed-integer programming model that optimally solves a new variant of the FRVCP, the FRVCP with nonlinear energy management (FRVCP-NLEM). It considers charging times as a nonlinear function and allows to decide at which speed to drive on each segment of the route while considering the non-linearity of energy consumption functions. The non-linearity of all functions has been solved using multiple linear approximations. The proposed model is tested using an electric vehicle trip planner called PlaniCharge that uses realistic energy consumption and charging functions that take into account external factors such as temperature and road topology. The model is tested under different road types such as urban or highway routes. The proposed model is able to optimally solve most test instances within seconds. Results show that varying the vehicle speed is an important factor to consider under low temperatures and for long-range routes as it can reduce total route duration. Some routes cannot be completed at maximum speed and require varying driving speed on segment to be able to reach the destination.
Research on Double Edge Detection Method of Midsole Based on Improved Otsu Method

The midsole is an important part of the shoe, but in its industrial production, the surface quality of the midsole currently relies on manual testing. It cost high and cannot meet the needs of industrial online real-time detection. To realize online surface defect detection, extracting the double edges of the midsole resulting from its special structure becomes an indispensable pre-work. This paper proposes a two-step Otsu method (TT-Otsu) to extract double edges of products. This method adopts the improved Otsu method to process the midsole image in two steps. It respectively combines with the Weighted Object Variance method (WOV) and the Neighborhood Valley-Emphasis method (NVE) to calculate the optimal threshold. Then the image is segmented to extract the edges and the misclassification error (ME) is 0.0007. To ensure the accuracy of the edge detection, the neighborhood gradient extreme value discrimination method is used to realize the local self-checking and make appropriate adjustments to the deviated edge. The false positive rate (FPR) and the false negative rate (FNR) of TT-Otsu are approximately equal to 5%. This method can effectively and clearly extract the double edges of the midsole. The precision rate is 95.61%, the average running time is 1.8s. The experiment demonstrates that the proposed method in this paper has good detection performance and good applicability.
COVID-19 diagnosis in CT images using CNN to extract features and multiple classifiers

Coronavirus disease (COVID-19) has already infected more than 20 million people worldwide and is responsible for more than 744,000 deaths. A major problem faced in the diagnosis of COVID-19 is the inefficiency and scarcity of medical tests. The use of computed tomography (CT) has shown promise in the evaluation of patients with suspected COVID-19 infection. The analysis of the CT examination is complex and requires the effort of a specialist, which can lead to diagnostic errors. The use of CAD systems can minimize the problems generated by the analysis of CTs by specialists. This article presents a methodology for diagnosing COVID-19 using a trainable resource extractor using CNN and multiple classifiers. First, the quality of the images was improved using histogram equalization and CLAHE. Then, a basic CNN is used to extract resources from 708 CTs, 312 with COVID-19, and 396 Non-COVID-19. After the extracted data, we used multiple classifiers for classification in COVID-19 and Non-COVID-19. The results show an accuracy of 97.88%, recall of 97.77%, the precision of 97.94%, F-score of 0.978, AUC of 0.977, and kappa index of 0.957. The results obtained show that the proposed methodology can be used as a CAD system to aid in the diagnosis of COVID-19.
Chance-constrained Two-stage Energy Hub Cluster Configuration for Integrated Demand Response Considering Multi-energy Load Uncertainty

In this paper, we study the configuration problem of Energy Hub cluster for integrated demand response (IDR) while considering multi-energy load (i.e., electricity/heat load) uncertainty. We first present a chance-constrained two-stage model for energy hub cluster configuration based on bilinear reformulation using a finite number of stochastic scenarios. Then, bilinear terms are exactly linearized to derive a mixed integer linear programming (MILP) model. Finally, the bilinear Benders decomposition (BBD) algorithm is utilized to compute the proposed MILP model. Numerical results demonstrate that our proposed model can effectively manage the multi-energy load uncertainty for energy hub cluster configuration and improve the performance of IDR. BBD algorithm is also computationally efficient than commercial solvers (such as CPLEX).
Multi-Scale Driver Behaviors Reasoning System for Intelligent Vehicles Based on a Joint Deep Learning Framework

The mutual understanding between driver and vehicle is critically important to the design of intelligent vehicles and customized interaction interface. In this study, a deep learning-based joint driver behavior reasoning system toward multi-scale and multi-tasks behavior recognition is proposed. Specifically, a multi-scale driver behavior recognition system is designed to recognize both the driver's physical and mental states based on a deep encoder-decoder framework. The system jointly recognizes three driver behaviors, namely, mirror-checking, lane change intention, and emotions based on the shared encoder network. The encoder network is designed based on a deep convolutional neural network (CNN), and several decoders for different driver states estimation are proposed with fully connected (FC), and long short-term memory (LSTM) based recurrent neural networks (RNN), respectively. The proposed framework can be used as a solution to exploit the relationship between different driver states for intelligent vehicles towards an efficient driver-side understanding. The testing results on the Brain4Car dataset show accurate performance and outperform existing methods on driver postures, intention, and emotion recognition.
Product Biography Information System: A Lifecycle Approach to Digital Twins

This paper introduces the concept of product biography information system (PBIS). The findings emerge from a design science research project conducted in a paper pulp company integrating the PSI-20 stock market index of Euronext Lisbon. The results include a reference architecture and design principles for the development of PBIS supported by blockchain and multiple generations of digital twins. For theory, PBIS offers an extension to product lifecycle management, ensuring that products lives are memorized in its sociomaterial context of production, logistics, and use. For practice, we show how organizations can design new enterprise systems that capture complex product biographies taking advantage of Industry 4.0. The findings are relevant for the innovation of product-service systems that adhere to the emerging challenges of sustainable development and traceability.
The identification of variable-length coevolutionary patterns for predicting HIV-1 protease cleavage sites

The substrate specificity of human immunodeficiency virus 1 (HIV-1) plays an essential role in designing HIV-1 inhibitors for therapy purpose. Hence, to predict the existence of cleavage sites in HIV-1 protease, a variety of computational algorithms have been developed by following the homogeneous information in substrate sequences. However, few of them can fully exploit such information, as they are not capable of identifying variable-length coevolutionary patterns. To overcome this limitation, we propose a novel algorithm with which variable-length coevolutionary patterns can be identified. Based on these patterns, we compose the feature vector for each of substrates and train the SVM classifier to the purpose of predicting HIV1 protease cleavage sites. Experimental results show that the use of variable-length coevolutionary patterns can improve the prediction performance in terms of AUC and PR-AUC analysis.
Long Range Underwater Localization and Navigation using Gravity-Based Measurements

This paper reports on work to assess the feasibility of gravity-based long range underwater navigation and localization. As a first step, this is explored in simulations with RAO-Blackwellized particle filter simultaneous localization and mapping (SLAM). When implemented on an autonomous underwater vehicle it can operate submerged for extended periods without the use of an active sensor, thus widening the variety of AUV missions. Additionally, this work applies information theory to navigate through a region such that the SLAM data association, and thus the localization, performance is improved. The results also indicate that characteristic values for a region can be used as a SLAM metric for the region. Combining the characteristic value with information theory techniques improves the localization performance at extended ranges and is a first step towards long range underwater localization using gravimeters. Future work will optimize the particle filter, explore more sophisticated loop closures as well as hardware-in-the loop tests.