Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overset Mesh Framework for an Isentropic ALE Navier-Stokes HDG Formulation

Fluid-structure interaction simulations where solid bodies undergo large deformations require special handling of the mesh motion for Arbitrarily Lagrangian-Eulerian (ALE) formulations. Such formulations are necessary when body-fitted meshes with certain characteristics, such as boundary layer resolution, are required to properly resolve the problem. We present an overset mesh method to accommodate such problems in which flexible bodies undergo large deformations, or where rigid translation modes of motion occur. To accommodate these motions of the bodies through the computational domain, an overset mesh enabled ALE formulation for fluid flow is discretized with the hybridizable discontinuous Galerkin (HDG) finite element method. The overset mesh framework applied to the HDG method enables the deforming and translating dynamic meshes to maintain quality without remeshing. Verification is performed to demonstrate that optimal order convergenceO (k + 1) is obtained for arbitrary overlap and approximation order k.
Joint Credibility Estimation of News, User, and Publisher via Role-Relational Graph Convolutional Networks

The presence of fake news on online social media is overwhelming and is responsible for having impacted several aspects of people’s lives, from health to politics, the economy, and response to natural disasters. Although significant effort has been made to mitigate fake news spread, current research focuses on single aspects of the problem, such as detecting fake news spreaders and classifying stories as either factual or fake. In this paper, we propose a new method to exploit inter-relationships between stories, sources, and final users and integrate prior knowledge of these three entities to jointly estimate the credibility degree of each entity involved in the news ecosystem. Specifically, we develop a new graph convolutional network, namely Role-Relational Graph Convolutional Networks (Role-RGCN), to learn, for each node type (or role), a unique node representation space and jointly connect the different representation spaces with edge relations. To test our proposed approach, we conducted an experimental evaluation on the state-of-the-art FakeNewsNet-Politifact dataset and a new dataset with ground truth on news credibility degrees we collected. Experimental results show a superior performance of our Role-RGCN proposed method at predicting the credibility degree of stories, sources, and users compared to state-of-the-art approaches and other baselines.
An Action-based Model to Handle Cloning and Adaptation in Tabular Data Applications

Many software systems require diverse data gathering and handling through processes that manipulate tabular data, often with a spreadsheet orientation. Variability in tabular data cannot be captured in a complete up-front analysis as everything is done at the final user level. She progressively adapts or clones some tabular data organized to conduct a process. Consequently these organized data are constantly both a final usable product and a potential candidate for cloning. This huge diversity, the high frequency of their evolution over time, and the intrinsic need to use cloning lead naturally to the usage of a clone-and-own approach with well-known negative impacts on maintenance and quality. In this paper we advocate that this can be replaced by controlling the clone-and-own process with provenance information that completely captures, at the domain level, the cloning actions and the adaptations applied on a product defining its clones. Each action over the process, its observations, and its data are captured in a complete model through traces of atomic adaptations, complemented with specific derivation and extraction actions. This model enables obtaining the whole history of both data and processes over time, as well as the accountability of variability-related actions. We report on a study showing the relevance of tackled problem in a variability-rich agronomy software of an industrial partner. We also show that a first prototype covers the extracted usage scenarios, from simple and entire cloning to more complex partial cloning.
Predicting strategy choice in word formation: A case study of reuse and compounding

Natural language expresses new concepts by reusing existing words or coining new ones. Previous studies have examined these word formation strategies separately through a functional lens, but it is unclear why one strategy might be preferred over another. In this study, we hypothesize that communicative and cognitive efficiency might predict the choice between lexical reuse and compounding for expressing an emerging concept. We test our hypothesis by developing a computational analysis of English word meanings that emerged over the past century. Our results suggest that strategy choice may be explained partly by a pressure for least effort. Our work contributes a novel connection between strategy choice in word formation and functional theories of language.
Effects of augmented exercise therapy on outcome of gait and gait-related activities in the first 6 months after stroke: a meta-analysis

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CZECH TECHNICAL UNIVERSITY IN PRAGUE Faculty of Electrical Engineering

This thesis deals with a physical interaction of a human and an unmanned multirotor vehicle. A specialized platform that allows a safe interaction is designed and built. The dynamical model the designed platform is presented, and its parameters are identified. The main focus of this work lies in the design of a state observer capable of an external force estimation. Further, a control design suitable for physical interaction is proposed, using the Admittance control. The functionality of the developed system is verified in the series of simulation experiments.
Energy Eficient Transmission Techniques in Continuous-Monitoring and Event-Detection Wireless Sensor Networks 97 Energy Eficient Transmission Techniques in Continuous-Monitoring and Event-Detection Wireless Sensor Networks

Wireless Sensor Networks (WSNs) can be typically used to achieve Continuous Monitoring (CM) or Event-Detection Driven (EDD) inside the supervised area. For both applications, sensors consume energy for three main reasons: sensing, processing and wireless communicating. The wireless communication refers to data transmission and reception. Among these three operations, it is known that the most power consuming task is data transmission. Approximatively 80% of power consumed in each sensor node is used for data transmission. Hence, unnecessary transmissions and/or unnecessary large data packets reduce the system’s lifetime. In this work, we are interested in studying different data transmission schemes that reduce the energy consumption by means of compression, in order to reduce the data packet’s length, or by means of avoiding transmission of redundant information. Continuous-monitoring applications require periodic refreshed data information at the sink nodes. To date, this entails the need of the sensor nodes to transmit continuously in a periodic fashion to the sink nodes, which may lead to excessive energy consumption. In this work, we show that continuous-monitoring does not imply necessarily continuous reporting. Instead, we demonstrate that we can achieve continuous-monitoring using an event-driven reporting approach. For example, consider a continuous-monitoring temperature application, where each sensor node transmits periodically the sensed temperature to the sink node. In such application, it may happen that sensors have very similar reading during long periods of time and it would not be energy-efficient for sensors to continuously send the same value to the sink node. The network lifetime would be greatly increased by programming the sensors to transmit only when they have sensed a change in the temperature compared to the last transmitted information. In doing so, the end user would have a refreshed value of the temperature in the supervised area even if the sensors are not transmitting continuously in a periodic fashion. The final user would have exactly the same information gathered by the WSN as with the classical continuous-monitoring applications, but while the sensors only transmit when there is relevant data. 5
Non-Catalytic and Catalytic Conversion of Fruit Waste to Synthetic Liquid Fuel via Pyrolysis

Plum stone stands out as an alternative biomass source in terms of obtaining fuel and chemicals with or without catalysts under different conditions. Under variable heating rates (10, 50, and 100 °C min−1) and pyrolysis temperatures (400, 450, 500, 550, and 600 °C), plum stone was pyrolyzed at a constant rate in a constant sweep gas flow (100 cm3 min−1) in a tubular fixed-bed reactor. According to the results, an oil yield reaching a maximum of 45% was obtained at a heating rate of 100 °C min−1 and pyrolysis temperature of 550 °C in the non-catalytic procedure. The catalytic pyrolysis was carried out with two selected commercial catalysts, namely ZSM-5 and PURMOL-CTX and clinoptilolite (natural zeolite, NZ) under optimum conditions with a catalyst ratio of 10% of the raw material. With the addition of catalyst, the quantity and quality of bio-oil increased, including calorific capacity, the removal of oxygenated groups, and hydrocarbon distribution. In the presence of catalysts, an increase was observed in terms of desirable products such as phenol, alkene, and alkane, and a decrease in terms of undesirable products such as acids. Considering and evaluating all the results, the use of zeolite materials as catalysts in pyrolysis is a recommended option for obtaining enhanced chemicals and fuels.
Lysimeter Studies and Irrigation of Oil Palm in Some Inland Soils of Peninsular Malaysia - Felda's Experience*

Rainfall distribution in inland areas of Peninsular Malaysia is generally uneven, thus bearing considerable effect on the growth and production of oil palm. A single lysimeter study conducted in 1974 at Felda’s Tun Razak Agricultural Services Centre (3° 52' 55" North, 102° 43' 41" East) at Jerantut, Pahang over a period of 33 years revealed a strong influence of water on oil palm yield. In this study, the estimated potential evapotranspiration (PET) for immature oil palm was 4.5 to 5.0 mm per day and 5.0 to 5.5 mm per day for mature palms. During drought, the PET could increase to 6.5 to 7.5 mm per day whereas in the monsoon season the rate could be as low as 3.0 to 3.5 mm per day. During an El Nino, the PET could reach as high as 10 mm per day. The highest yield obtained for the single irrigated palm was an equivalent of 59 tonnes FFB per hectare achieved in a particular year. Results over 33 years showed that the yield was 57 per cent higher with irrigation than without. On the same subject, Felda Agricultural Services Sdn Bhd laid out three field trials to investigate the effect of irrigation on oil palm performances under different rainfall regimes and palm age. In a distinctly drier environment in Serting Hilir, Negeri Sembilan, which involved 21 ha of clonal oil palm, the response of irrigated palms over 23 years showed an average yield increase of almost 12 tonnes per hectare (or 56%) over the non-irrigated palms. The clonal block sustained yields of more than 30 tonnes per hectare since inception of the irrigation or more than 18 years. Irrigation improved the oil yield components. The cost-benefit and viability of irrigation based on to-date data from these trials is given.
A Real-Time Robot Location Algorithm Based on Improved Point-Line Feature Fusion

The traditional robot real-time positioning and mapping (SLAM) algorithm has the problem of over-extraction of line segments in dense environments, which leads to mis-matching and reduces the accuracy of the system. To solve this problem, this paper proposes a visual SLAM method called IPLI-SLAM based on an improved point-line combination tightly coupled with IMU. First of all, Shi-Tomasi feature is used for point feature extraction. Secondly, in the aspect of online extraction, the LSD line detection method is modified to the accuracy of the algorithm. The system sets a filter before line extraction. The filter is based on the pixel gradient density setting to filter the environment with more line textures to reduce the error matching of the algorithm. Finally, the point-line visual information is tightly coupled with IMU and then added to the back-end for optimization. By testing eight different sequences in the open source dataset EuRoc with three difficulty levels: easy, medium and hard, the results show that the trajectory RMSE value (root mean square error) of this algorithm is reduced by 50.7% compared to the VINS-mono algorithm and by 13.2% compared to the PL-vins algorithm, which fully demonstrates the effectiveness of this algorithm.
Comprehensive Analysis and Modeling of Conventional and Hybrid Electric Ferry Power and Propulsion Systems

Stricter emission regulations, increased fuel prices, and technological advancements in power electronics motivate engineers to explore new hybrid and electric propulsion and power systems and replace the conventional diesel engine propulsion commonly used in ships. Ferries with hybrid power systems (HPSs) and direct current (DC) distribution are gaining more interest due to their capability to reduce fuel consumption and emission. This paper presents a detailed modeling of mechanical and hybrid electric propulsion systems.
Need a dog for seeing eye? A Walk Viewpoint Dataset for Freespace Detection in Unstructured Environments

Freespace Detection (FD) is crucial for robust and safe autonomous navigation. However, existing datasets usually concentrate on structure road environments. The FD in unstructured environments, e.g., walk assistance for the visually-impaired, has been rarely investigated. In this paper, We propose a novel dataset called the Walk Viewpoint Dataset (WVD). Different from the previous datasets, we focus on the walk viewpoint, where FD can provide the potential for improving the walking of visually impaired people. The target regions of WVD are annotated with 20 categories by fine-grained labels, which consist of 3,737 images and depth images. Moreover, we propose a new annotation hierarchy, which allows different degrees of complexity and creates opportunities for new training methods. Finally, our study provides the statistical analysis of label characteristics and baseline analysis, which demonstrates its distinction compared to previous datasets. The dataset can be accessed through the project pages: http://www.sensingAI. com.cn.
Marine Target Detection by Exploiting Multi-Circle Features with Convolutional Neural Network

Marine target detection with coastal radars is challenging, due to the strong sea clutter and the sidelobe of land clutter and city clutter. The non-uniform non-stationary and non-Gaussian clutter degrades the performance of traditional power-based threshold detection methods based on the statistical signal processing theory. Most recent works on the feature-based target detection methods relies on the time-frequency features, which may be unavailable for short-dwell-time applications. Therefore, this work develops a new multi-dimensional feature of targets, multi-circle feature, without limitation of the dwell-time length. A convolutional neural network (CNN) is employed to discriminate true targets and false alarms after the low-threshold detection in energy domain. The experimental results exhibit that the proposed CNN-based detection can achieve a nearly 80% reduction in false-alarm rate without the loss in detection rate compared to the traditional threshold detection.
Attention Based Network with DA-Loss for X-ray Contraband Automatic Detection

X-ray security check is one of the most effective security measures widely used in airports, high-speed trains, subways, and other important places. Due to the difference in imaging mechanism between X-ray images and visible images, X-ray contraband detection suffers from problems of large intra-class differences and small inter-class differences. The Softmax loss does not encourage compactness within class and separability between classes explicitly, resulting in insensitivity to target. We address this problem by proposing an attention mechanism based ACD-Net, which can quickly focus on the obscured objects in luggage. To optimize inter-class interval, we evaluate impacts of several cosine-based loss function on X-ray contraband detection performance, including Cosine Embedding Loss, L-Softmax Loss, CosFace and ArcFace. Then we propose a cosine loss function DA-Loss focusing on intra-class compactness and inter-class difference at the same time. Extensive experiments show the superiority of our proposed ACD-Net model with DALoss classification loss function, which has better separability of large intra-class differences and small inter-class differences, and outperform the state-of-the-arts.
Stress Detection Using Physiological Signals Based On Machine Learning

Stress can be defined as the body's attempt to control itself in response to changes in the environment. Due to stress work performance may suffer and the risk of neurological issues such as hypertension and psychological illnesses such as anxiety disorder may rise. In today's world, an increasing number of people are experiencing some form of stress. Comprehension of stress cognition is required along with the capacity to build systems with stress cognition characteristics. A methodology of stress detection using physiological signals based on machine learning is presented in this paper. Physiological signals such as respiration, sweat gland activity on the skin of hands, heart rate, and electromyogram were recorded while driving from multiple healthy participants in various situations and locations. The signal is then segmented for various time intervals such as 100, 200, and 300 seconds, depending on the levels of stress. Statistical features were retrieved and made available to the classifiers namely Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) algorithm. We achieved the highest accuracy of 96% with 100 and 200-second long signal, and 98% with 300-second long signal.
Muscle-Synergy-Based Signal Rectification Coping with sEMG Sensor Failure for Upper Limb 3D Voluntary Force Estimation

Myoelectric control has been wildly used in rehabilitation robots and prosthetic devices. However, surface electromyography (sEMG) electrodes may be detached since human limbs move with the robot during rehabilitation training\prostheses assistance. This kind of sEMG sensor failure may lead to serious accidents in which the robot obtains abnormal input signals and responds unexpectedly. To cope with the sEMG sensor failure problem, a muscle-synergy-based activation rectification method was proposed in this study to reconstruct the impacted muscle activation using other non-fault channels. The rectified muscle activation signals with the joint angle signals were then used to estimate the three-dimensional (3D) end force of the upper limb via a deep LSTM network (DLN). The proposed method was compared with the method without signal rectification during voluntary force estimation. The experiment demonstrated that the proposed method could rectify the muscle activation signals and had better force estimating accuracy comparing with the no-rectified method. This study may help the upper limb rehabilitation robots\prostheses accurately estimate the 3D voluntary force of users even when sensor failure occurs, thereby ensuring the normal progress of rehabilitation training\prostheses assistance.
'Who Done It?’ A Multidisciplinary Mobile Device Forensics Framework: From Theory to Practice with Intermediate Students

It is essential to initiate a curriculum concerning mobile device forensics and law enforcement in the intermediate grades in Mississippi using a facet of multidisciplinary subjects: biology, chemistry, computer science, criminology, and physics. The students participated in engaged teams who gathered the evidence (critical thinking), analyzed the evidence (deductive reasoning), and drew conclusions (inference) in a story-based scenario entitled “Cyberbullying Mobile Device Criminal Investigation. ” This research presents cyberbullying while bringing awareness to the law enforcement community by understanding digital forensics; furthermore, it shows how STEM and Criminology are presented to intermediate students by solving a middle school mystery based on a multidisciplinary framework. By including a multidisciplinary curriculum in this process, students developed an appreciation of the interrelatedness between the STEM Sciences and Liberal Arts Education.
A Novel Control Scheme for Harmonic Compensation in PV-Integrated System

Non-linear loads are frequently used in both domestic and industry-based applications. Currents with harmonic content are obtained when a non-linear load is coupled to the grid. These harmonics deteriorate the quality of the grid's voltage and current. A Proportional Integral-Modified Reduced Order Generalized Integrator based frequency lock loop (PI+MROGI-FLL) is developed for regulating the interconnecting inverter to mitigate the harmonics. The PI+MROGI-FLL is intended to analyze 3⊘reference currents by isolating the fundamental constituents from load currents. It offers many benefits, including improved harmonic mitigation, adaptable frequency and phase, synchronisation of the grid, and minimal computational burden. The suggested Controller is modelled in MATLAB/Simulink using a photo-voltaic system. The proposed controller's performance is compared with that of existing conventional controllers. In comparison to other controllers, the PI + MROGI controller exhibits higher harmonic mitigation capability.