Miracle-dz/UniFAR-FTUs huggingface dataset:
A non-contact SpO2 estimation using a video magnification technique

In this paper, we present a new non-contact strategy to estimate the Peripheral Oxygen Saturation (SPO2) based on the Eulerian motion video magnification technique and a signal processing technique, The magnification procedure was carried out using two approaches : the Hermite decomposition and the Gaussian decomposition. The SpO2 is estimated from the signals extracted after magnification process using the red and the blue Chanel of the frame. We have tested the method on five healthy subjects using videos obtained from the google-meet video conference platform. To compare the performance of the methods, we compute the mean average error and metrics issues from the Bland and Altman analysis to investigate the agreement of the methods with respect to a contact pulse oximeter device as reference. The proposed solution shows an agreement with respect to the reference of most of 98%
Second Language Learners’ Competence of and Beliefs About Pragmatic Comprehension: Insights From the Chinese EFL Context

It can be a great challenge for second language (L2) learners to comprehend meanings that are implied in utterances rather than the surface meaning of what was said. Moreover, L2 learners’ attitudes toward pragmatic learning are unknown. This mixed-methods study investigates L2 learners’ ability to comprehend conversational implicatures. It also explores their beliefs about and intentions to develop this ability using Ajzen’s theory of planned behavior (TPB). A total of 498 freshmen from a public university in China participated in the study. Data were collected using a web-based test, stimulated recall tasks and semi-structured interviews. Results show that the participants differed in recognizing the intended meanings. Complicated factors account for the variations. In addition to the types of implicature, learners’ beliefs about developing pragmatic comprehension also influence their learning intention, and subsequent performance. These beliefs include learners’ multi-layered, complex attitudes toward the outcomes of pragmatic learning, perceived self-efficacy beliefs regarding language proficiency and L2 cultural knowledge, actual behavioral control over opportunities and resources for pragmatic learning, and perceptions of less social pressure on pragmatic learning. The use of TPB may help language teachers and test designers to understand learners’ beliefs about L2 pragmatic learning in the English as a foreign language (EFL) context. Understanding the factors influencing learners’ intention will help design more effective teaching curricula that may integrate pragmatic instruction and testing in the future.
Principles of Soil-Vehicle Mechanics

The object of soil-vehicle mechanics is defined and existing theories briefly described. Experiments have shown that these theories work quite well for frictionless clay soils and for lightly loaded vehicles in firm soils where sinkage is small. For other soft soils performance cannot be predicted and the theory does not agree with experimental evidence.It is proposed that an improved and unified theory can be based on equations developed using the methods of soil mechanics, making appropriate allowance for the effect of compressibility. A resulting new pressure sinkage equation is shown to give better agreement with experiment.The complexities of the soil stress pattern imposed by vehicles are described and it is shown that these cannot be neglected. The effect of superimposing horizontal and vertical loads is to cause slip sinkage and experimental measurements show this to be important in sand but not in clay. An analysis is made which explains why this is so and gives insight into the physical nature o.
Tumor model parameter estimation for therapy optimization using artificial neural networks

Therapy optimization and personalization in cancer treatment requires reliable mathematical models. A key issue in personalization is the identification of the model parameters. We employ artificial neural networks to identify the model parameters based on few measurements using a priori information about the range of the parameters. The trainig data are generated in silico on known parameter intervals, taking into consideration the experimental setup we use to validate our results. The estimated parameters can be used to track the change of the parameters and can also be used as initial guesses for identification algorithms using local search.
Rec-clusterGCN: An Efficient Graph Convolution Network for Recommendation

With more Graph Convolutional Network (GCN) models applied to recommendation tasks, graph data of tremendous scalability becomes much harder to train for existing models. Many existing works of adapting GCN models to recommendation tasks often try to improve accuracy by aggregating messages from all high-order neighboring nodes, which can result in insufficient computational efficiency. To tackle with data with large scalability, we propose a novel recommendation algorithm—Rec-clusterGCN, which utilizes the cluster structure of graphs, and is suitable for SGD-based training. It goes as follows: at every step, a dense subgraph of user-item interaction will be constructed, and each node will only aggregate messages from neighboring nodes within the subgraph. Next, our model accepts the LightGCN model structure. It can reduce a huge amount of computational time cost and make training of larger graph possible without sacrificing too much accuracy. In addition, by using our node enhancement technique, the performance of Rec-clusterGCN is further improved. The experimental results also indicate that the proposed algorithm outperforms most baseline algorithms. Significant improvement has been made in computational efficiency (on average about 30.0% relative improvement for different layers in the Gowalla dataset).
Multi-scale Space-time Registration of Growing Plants

In this paper, we introduce a new method for the space-time registration of a growing plant that is based on matching the plant at different geometric scales. The proposed method starts with the creation of a topological skeleton of the plant at each time step. This skeleton is then used to segment the plant into parts that we call branches. Then these branches are further divided into smaller segments that possess a simple geometric structure. These segments are matched between two time steps using a random forest classifier based on their topological and geometric features. Then, for each pair of segments matched, a point-wise registration is devised using a non-rigid registration method based on a local ICP. We applied our method to various types of plants, including arabidopsis, tomato plant and maize. We established three different metrics for 3D point-wise shape correspondence to test the accuracy, continuity, and cycle consistency of the mapping. We then compared our method with the state-of-the-art. Our results show that our approach achieves better or similar results with a shorter running time.
Utilizing Game Engine for Development Interactive 3-Dimensional Geographic Information System (GIS) Agriculture Commodity Selection and Land Evaluation

This study develops an interactive 3-Dimensional Geographic Information System (3D-GIS) for agriculture. GIS's ability to visualize geographic conditions is very crucial for agriculture. The purpose of this study was to explore the Unity3D to provide large-scale GIS data. This study has four stages. The first stage is data gathering, the second stage is data integration, the third stage is data visualization design, and the last stage is GIS interaction design. Unity3D can visualize the virtual environment. Unity3D also provides a rich set of libraries and assets for user interaction. However, building a GIS requires a projection / coordinate system. Mapbox extension can provide georeferencing services for Unity3D. Mapbox also makes it possible to overlay vector layers in the same way as GIS software. Utilizing a game engine for GIS development is expected to enrich GIS with more interactive content visualization techniques. With interactive maps, it is hoped that it will make it easier for farmers to understand the information presented on GIS. Furthermore, by utilizing the game engine in making GIS, it is expected that GIS will have a high level of usability.
Incorporating assessed style, length, and strength into breeding objectives for fine and superfine Merino flocks.

In fine and superfine flocks, style, staple length and staple strength influence fleece value. Evaluation of the consequences of including these traits in breeding objectives highlights again that if traits of economic importance are ignored when formulating objectives, inappropriate selection decisions can result. For a fleece value objective that contained all traits of importance, near optimal aggregate gains were achieved with an index containing clean fleece weight, mean fibre diameter, coefficient of variation of fibre diameter and style.
Classifying EEG motor imagery signals using supervised projection pursuit for artefact removal

A novel bottom-up approach for EEG signal artefact removal and classification is presented. An eigenbrain is constructed from two eigenchannels on a per subject basis. An eigenchannel provides a characteristic vector space to discriminate motor imagery from resting state signals in both the µ and β frequency bands. Discrimination between left and right hand motor imagery is achieved at the eigenbrain level as a direct sum of the eigenchannel vector spaces. It was found this methodology is viable using only 5% of available motor imagery trials during training, yielding 92.6% classification accuracy on the selected test subject. Furthermore, the utility for real-time applications is promising with a rapid classification that takes less than 100 ms after the initial calibration procedure is completed.
Improving user experience of SSVEP-BCI through reduction of stimuli amplitude depth

Steady-States Visually Evoked Potentials (SSVEP) is currently one of the most widely used paradigms in Brain-Computer Interfaces (BCI). Although the SSVEP-BCI are characterized by their high and robust classification performance, the repetitive presentation of flickering stimuli is uncomfortable from a user experience perspective point of view. Indeed, the low-level visual features of SSVEP stimuli make them straining to the eyes over time and could be disruptive to the execution of tasks requiring sustained attention. They could even induce epileptic seizures. This study explores the reduction of the stimulation amplitude depth (a magnitude diminution of 90%) for the design of SSVEP stimuli as a solution to improve user comfort. The classification accuracy obtained by different pipelines was systematically compared between low amplitude and standard full amplitude SSVEP stimuli. The results reveal high classification accuracy for both high (99.8%) and low magnitude (80.2%) stimuli using the Task-Related Component Analysis (TRCA) classification method. The present findings demonstrate the validity of reducing SSVEP stimuli amplitude to increase users’ comfort paving the way for transparent BCI operation.
A Study on Reward and Punishment Learning Using A Data-Driven Approach

Mental stress is the main well-being problem worldwide today. It is responsible for most of all mental-brain diseases. It does not need any specific reason to happen. It can be experienced from a very little incident to a huge incident. The consequence of it depends on how people handle it. Depression and anxiety are the results of mental stress, and they are the major challenges in today’s world. Depression and anxiety are the leading causes of suicide. Most of the time suicidal patients hide their true feelings and fail to communicate their psychiatric problems to physicians. The specific issues that need to be addressed are finding an easy, reliable and realistic way to diagnose mental stress to keep it from becoming a serious and irreversible condition. The primary prevention of mental stress utilizing machine learning algorithms based on reward and punishment processing is important to avoid mental diseases. Several techniques have been used to detect mental stress, and very few papers have tried to detect a patients’ comorbidity condition. However, literature shows that there are still chances of further improvement in this field. The traditional methods of detecting mental stress involve a statistical questionnaire approach with some shortcomings as results based on the traditional approach can be biased, which is not possible if Electroencephalogram (EEG) signals are used. Therefore, in this paper, we proposed a method to evaluate the EEG signals on thirty-two individuals for identifying comorbid patients using nine machine learning classifiers based on reward and punishment processing. The performance of our method is also shown to be better than some existing methods.
Proposal of a Neuromusculoskeletal Model Considering Muscle Tone in Human Gait

In an aging society, it is increasingly important to solve problems related to human gait. One of these issues is the relationship between gait and muscle tone. In this study, we propose a neuromusculoskeletal model that can be used to represent and evaluate the movement changes generated by changes in muscle tone in human gait using computational simulations. The model is based using a musculoskeletal model with 70 muscles and a neurological controller model that can represent muscle tone. As a hypothesis to judge the validity of the model, a high muscle tone makes it difficult to maintain gait and makes the stride length narrow. A total of 1470 parameters were optimized for 3 days for each trial with varying muscle tone. As a result under the condition of high muscle tone, the gait maintenance time became shorter, and the stride length became narrow compared with the experimental value. Therefore, the hypothesis used to judge the validity of the model that gait maintenance becomes difficult and stride length becomes narrow under high muscle tone was proved. In addition, the tendency for changes in the angle of the knee joint, the distance traveled by the center of the foot pressure, the number of times the foot touched the ground, the distance the foot swung forward, the regularity and periodicity of each gait, and the bimodality of foot pressure were evaluated and confirmed to be natural. From these results, we conclude that the neuromusculoskeletal model for gait proposed in this study was proper.
Augmenting MBSE with Digital Twin Technology: Implementation, Analysis, Preliminary Results, and Findings

Model-Based Systems Engineering (MBSE) requires more significant investment than traditional systems engineering in the early system life cycle phases. Program management justifies this additional investment by arguing that such investments can be expected to produce continuous gains across later phases of the systems life cycle resulting from early detection of defects, risk reduction, improved communication, superior supply chain integration, product line definition, and enhanced traceability. Since systems continue to evolve over their life cycle, system models need to be updated continually to reflect the system's current state and thereby sustain value. However, in today's systems engineering organizations, the current practice is to reallocate modeling resources to other projects once initial modeling on a particular project is completed. This practice results in a resource shortfall that impedes the ability to continuously update system models through the later phases of the system life cycle. This paper presents how digital twin technology can be exploited within MBSE to ensure continuous model updates throughout the system life cycle. This paper also presents preliminary results from prototyping and experiments conducted with a digital twin that is continuously updated with data from the physical system operating in the real world. Finally, this paper shows how operational analysis and system modeling can be significantly enhanced by leveraging digital twin technology within MBSE.
One-Class Detection and Classification of Defects on Concrete Surfaces

Today, railway infrastructure is subject to regular inspections carried out by image acquisition. Experts need automatic tools to process these images and extract defects. These defects can be classified in two categories : linear defects, like cracks, and surface defects, like spall, humidity, graffiti… Cracks detection has been presented in a previous work, and is based on Local Binary Patterns (LBP) extraction and our FLASH algorithm [1]. In this paper, we propose a new method, using the same LBP descriptors, already extracted, to detect surface defects using generative adversarial networks (GAN). The detection is followed by a classification with a potentially scalable system. Experiments show that our detector achieves better performance than the state-of-the art in application to defect detection.
Covid-19: Babies born during the pandemic show slight development delays

Dani Dumitriu, the lead investigator and an assistant professor of paediatrics and psychiatry, said, “We were surprised to findabsolutelyno signal suggesting that exposure to covid while in utero was linked to neurodevelopmental deficits. Rather, being in the womb of a mother experiencing the pandemic was associated with slightly lower scores in areas such as motor and social skills, though not in others, such as communication or problem solving skills. ” She added, “The results suggest that the huge amount of stress felt by pregnant mothers during these unprecedented times may have played a role. ”
Personalized Design and Experimental Validation of a Limited Information Cooperative Shared-Controller for Vehicle-Manipulators

Large vehicle-manipulators are systems consisting of a medium-sized heavy-duty vehicle and a hydraulic manipulator. They operate in an unstructured environment and are therefore not fully automated. However, semi-automation of such a system is possible, where the automation controls the vehicle and a human operator controls the manipulator. Since in the unstructured environment not all system trajectories are measurable for the automation, a so-called Limited Information Cooperative Shared-Controller (LICSC) has been proposed in previous work. However, the design of the LICSC is done through tuning which is sensitive to the operator controlling the manipulator. The first contribution of this work is to reduce this sensitivity. A novel personalized design of the LICSC is presented that provides control tailored to the human operator. Our proposed approach uses state-of-the-art design methods of a cooperative controller to obtain the LICSC. The second contribution is the experimental validation of the LICSC, which proves the importance of the personalization to the human operator and demonstrates the advantage of the method.
Comparative Study of Different Activation Functions for Anomalous Sound Detection

Anomaly detection is of great importance in our modern life as it can be very useful in many ways such as lowering costs, avoiding potential injuries, and even saving lives. The method of anomalous sound detection using the Self-Supervised Learning (SSL) approach is effective and has a relatively low training time with the use of a Convolutional Neural Network (CNN). The use of techniques such as the pre-training and hard sample with the SSL approach led to producing very high results scoring results higher than 0.9 for the Area under the curve (AUC) score especially for the non-stationary sounds. An AutoEncoder (AE) based system developed by the Detection and Classification of Acoustic Scenes and Events (DCASE2020) competition’s organizers were used for comparative purposes to compare the results of the SSL method with the results of the baseline system. The results of the baseline system and the results of the SSL approach with different configurations and the SSL method had shown higher results in most cases.
Deep Reinforcement Learning Algorithm for Fast Solutions to Vehicle Routing Problem with Time-Windows

Vehicle routing problem (VRP) is a well known NP-hard combinatorial optimization problem having several variants. In this paper, we consider VRP along with additional constraints of capacity and time-windows (CVRPTW) and aim to provide a fast and approximately optimal solutions to large-scale CVRPTW problems. We present a deep Q-network with encoder-decoder based reinforcement learning approach to solve CVRPTW. The encoder is based on the attention mechanism whereas decoder is fully connected neural network. Via numerical experiments on benchmark datasets, we show the efficacy and computational speed our approach compared to baseline heuristics, a meta-heuristic algorithm, and a multi-agent reinforcement learning (RL) based framework.
Density estimation in x-ray computed tomography using the Alvarez-Macovski model

We introduce a method to extract density information from an x-ray computed tomography (XCT) volume that is more accurate than simply assuming density is proportional to CT number. XCT is a versatile tool for analysis, however, for lab-based XCT machines that employ polychromatic x-rays, it is difficult to extract anything more than the crudest quantitative data from the sample. Reconstructed tomograms values are, in theory, the x-ray attenuation coefficients of the material. However, due to the polychromatic nature of the beam, and effects such as beam hardening, such an interpretation of real data is rarely feasible. The Alvarez-Macovski (AM) equation, which is used in quantitative XCT reconstruction algorithms, provides a model of x-ray attenuation. We use the AM equation to extract quantitative information from conventionally reconstructed tomograms, provided it is not too severely affected by beam-hardening artefacts. In essence, we assume that the tomogram values are proportional to the attenuation coefficients of the AM equation at a mean x-ray energy. Then, given a calibration scan which contains enough materials, we can solve the the AM equation for the unknown coefficients and exponents. We then apply it to tomograms of objects with similar shape and material composition. The quantitative data extracted thus provides a more accurate estimate of both per-material density and bulk density.
Influence Green Innovation to Competitive Advantage and Finance Performance Indonesia State Own Entreprise

In the current situation, Indonesian state owned enterprises have not reached their performances, which means that their finance performances are relatively low during the late five year period. Their low finance performance are predicted of their competitive disadvantages compare to other private companies which may cause by inappropriate green innovation, either product innovation, process innovation, or market innovation. The objectives of this research were to get (1) the pictures of green innovations; (2) the correlation between the effect of green innovation to the competitiveness advantages; (3) the correlation between the effect of green innovation to the finance performances; (4) the correlation between the effect of competitiveness advantages to the finance performances in the Indonesian state owned enterprises. The methods used in the research were descriptive survey method and explanatory survey method with six proposed hypotheses. Unit analysis in this research is the whole of Indonesian state-owned enterprises.Model structural equivalent (SEM) is used for the analysis method. From the analysis results, it has been figured out that green innovation that has been done utilize their competitive resources but it isn’t optimalization (unmarket oriented). Green Innovation have a significant positive effect than the competitiveness advantages in the Indonesian state owned enterprises. Meanwhile Green Innovation has positif effect to the Finance performances in the Indonesian state owned enterprises. Competitive advantages have shown a significant positive effect to the marketing performances in the Indonesian state owned enterprises.