Miracle-dz/UniFAR-FTUs huggingface dataset:
Void Node Detection and Packet Re-routing in Underwater Wireless Sensor Network

Underwater wireless sensor networks (UWSNs) have been appeared as an encouraging innovation to screen and investigate the seas in lieu of customary undersea wire line instruments. All things considered, the information social occasion of UWSNs is still seriously restricted in light of the acoustic channel correspondence attributes. One approach to improve the information assortment in UWSNs is through the plan of directing conventions thinking about the special attributes of the underwater acoustic correspondence and the exceptionally unique organization geography. In this paper, we propose the GEDAR steering convention for UWSNs. GEDAR is an anycast, geographic and deft steering convention that courses information parcels from sensor hubs to numerous sonobuoys (sinks) at the ocean's surface. At the point when the hub is in a correspondence void area, GEDAR changes to the recuperation mode strategy which depends on geography control through the profundity change of the void hubs, rather than the customary methodologies utilizing control messages to find and keep up steering ways along void locales. Reproduction results show that GEDAR altogether improves the organization execution when contrasted and the pattern arrangements, even in hard and troublesome versatile situations of inadequate and extremely thick networks and for high organization traffic loads.
MDD4IoT: Model-Driven Development Framework Proposal for Internet of Things

The creation of Internet of Things (IoT) solutions is fairly complex, having to integrate and communicate between sensors, devices, and larger systems. Specifically, Cyber-Physical devices often lack software quality, resulting in hard-to-maintain solutions, presenting deficiencies in important aspects of IoT, such as portability and security. Software quality is of great importance to a solution’s long-term success, this work describes the proposal of a framework that aims to aid in the developing of these solutions, applying a Model-Driven inspired methodology. Resumo. A criação de soluções para Internet das Coisas (IoT) se trata de um processo complexo, integrando e conectando diversos sensores e dispositivos com sistemas maiores. Especificamente dispositivos Cyber-Fı́sicos frequentemente apresentam deficiências em aspectos importantes da IoT, como portabilidade e segurança. Qualidade de software é de grande importância para o sucesso desta soluções, este trabalho descreve a proposta de um framework que tem como objetivo auxiliar no desenvolvimento destas soluções, aplicando uma metodologia Dirigida a Modelo.
Learning and analyzing spatio-temporal objects from high resolution satellite image time series

Context On March 7, 2017, the European Space Agency (ESA) successfully put its latest highresolution satellite Sentinel-2B into orbit. The two Sentinel-2 satellites are now capturing images of all emerged surfaces every 2 to 5 days at high spatial and spectral resolutions, which makes it possible to monitor the evolution of land surfaces on a global scale. Satellite image time series (SITS) extracted from Sentinel-2 constellations are useful for many applications such as land cover mapping [1], crop type mapping, soil moisture estimation, burnt area detection, and many more. Similarly, high resolution satellite image time series, such as the one provided by Pléiades, are relevant for urban area analysis, disaster risk management, and rapid mapping.
Art Education and its Implementation: Factors Affecting Arts and Culture Education at Warren Park/Mabelreign Secondary Schools in Harare, Zimbabwe

The research focused on examining the factors affecting the implementation of Arts and Culture Policy in Warren Park/Mabelreign Secondary schools. The research made use of the interpretive paradigm since it is underpinned by observation and interpretation. The case study was used as the research design to provide the researcher with primary data. The data was collected through questionnaires as well as interviews with teachers, school heads, district education officers, parents and pupils. Among other things, the research established that most schools acknowledge that the Arts and Culture programme should be implemented according to Ministry specifications but they are not implementing it. The other key finding was that, there is a tendency amongst schools to give attention to Arts and Culture when schools are going to compete as well as when they are being visited by district and provincial education officers. There is also lack of full support in Arts and Culture from Ministry of Primary and Secondary Education, District Education officers, secondary school heads, Arts and Culture Heads of Department, secondary school teachers, parents and secondary school pupils. Findings also reveal that the policy on the institutionalisation of Arts and Culture was not matched with the provision of adequate materials, infrastructural facilities and work force. Consistent with the research objectives, the interviews focused on the challenges and opportunities presented in the implementation of Arts and Culture in schools.
On Dyadic Fractional Packings of $T$-Joins

Let G = (V,E) be a graph, and T ⊆ V a nonempty subset of even cardinality. The famous theorem of Edmonds and Johnson on the T -join polyhedron implies that the minimum cardinality of a T -cut is equal to the maximum value of a fractional packing of T -joins. In this paper, we prove that the fractions assigned may be picked as dyadic rationals, i.e. of the form a 2k for some integers a, k ≥ 0.
Ostwald Ripening in an Oxide‐on‐Metal System

Ostwald ripening is a well‐known physicochemical phenomenon in which smaller particles, characterized by high surface energy, dissolve and feed the bigger ones that are thermodynamically more stable. The effect is commonly observed in solid and liquid solutions, as well as in systems consisting of supported metal clusters or liquid droplets. Here, evidence is provided for the occurrence of Ostwald ripening in an oxide‐on‐metal system which, in this case, consists of ultrathin iron monoxide (FeO) islands grown on Ru(0001) single‐crystal support. The results reveal that the thermally‐driven sintering of islands allows altering their fine structural characteristics, including size, perimeter length, defect density and stoichiometry, which are crucial, for example, from the point of view of heterogeneous catalysis.
Development of Artificial Intelligence Algorithm for Computer Aided Diagnosis of Brain Tumour (CADbrat) Using Tensor Flow, TFlearn Library and Magnetic Resonance Images

Early diagnosis of a brain tumour is very important. It improves patient ’ s quality of life and offers diverse options for treatment and reduces the financial burden of tumour treatment. In recent years, computational resources have improved, and they are capable of handling large amount of data which are generated during clinical diagnosis. Furthermore, the recent advances in machine learning have made large data analysis in medicine more tractable and interesting. Automated deep learning-based computer-aided diagnosis (CADx) can be deployed as a crucial and reliable tool used for early detection. In this study, a CADx algorithm with high accuracy and sensitivity has been developed to provide assistance to health care givers in the timely diagnosis of brain tumours. The artificial intelligence algorithm made use of Tensor Flow, TFlearn Library and Magnetic Resonance Images as such model optimization was achieved by cascading the convolutional layers with a down-sampling layer (Max pooling) and higher filter number at the inception convolution layer to extract high level features and provide translational invariance down the network. With this model, an average F1-score of 99.49% was obtained despite training the network on a CPU. This amounts to a slightly improved performance of a similar model which was, however, trained on NVIDIA Tesla K40 server (2880 CUDA cores and 12GB memory). To demonstrate the application of the developed convolutional neural network (CNN) algorithms in clinical scenarios, a graphic user interface (GUI) using Flask application programming interface (API) was developed for easy brain tumour diagnosis with MRI scans.
Visual Navigation Method for Carrier-based Aircraft Landing

Carrier landing of fixed-wing aircraft is the most difficult and challenging task in the aerial field. Precise and reliable landing systems are the key to carrier landing. Visual navigation has the advantages of high precision of the relative navigation. Hence, it has become a promising method for landing systems. However, tracking the targets continuously and extracting the coordinates of the targets accurately are challenges for visual navigation due to the complex environment and dramatic distance changes during carrier landing. Therefore, a visual navigation method utilizing an airborne medium-wave infrared camera to track four infrared targets installed on the carrier, which can operate in low visibility weather conditions, is presented in this article. A segmentation detection method was adopted for tracking the targets. The kernel correlation filter was used to detect the targets when the aircraft was far from the carrier stern, which could quickly track targets with relatively constant dimensions. A convolutional neural network classifier based on transfer learning was used to detect the targets when nearing the carrier, thereby reducing the risk of losing targets due to severe dimensional changes. Finally, the relative position and attitude information could be obtained by the POSIT (pose from orthography and scaling with iterations) pose estimation algorithm. On-vehicle experiments were performed, and the results verified the effectiveness of the method.
Design and Research of Operating Repulsion Mechanism Suitable for 252kV High-Speed Breaking Circuit Breaker

This paper proposes a high-speed operating repulsive force mechanism suitable for 252kV high-voltage circuit breakers. The mechanism draws on the working principle of the electromagnetic repulsion operating mechanism of the medium voltage circuit breaker, and combines the new electromagnetic fast repulsion device with the traditional disc spring hydraulic mechanism to realize the fast action of the circuit breaker. This paper first introduces the working principle of the electromagnetic repulsion operating mechanism, and proposes the initial design scheme of the mechanism. Subsequently, the digital twin technology was used to establish the rigid-flexible coupling multi-body dynamics model of the mechanism, and the defects of the initial scheme were analyzed based on simulation. Finally, the paper optimized the design of the initial scheme and verified it by simulation. The test shows that the 252kV high-voltage circuit breaker with the improved scheme as the operating mechanism works normally and can complete the fault breaking within 20.1ms, which is twice as fast as the current products of the same type.
Study on Key Technical Schemes for Grid-connected Performance Testing of Offshore Wind Turbines

Offshore has the characteristics of not occupying land, rich in wind power resources, and has the advantages of high utilization hours of power generation and suitable for large-scale development. It has become the key direction of global wind power development. It can be predicted that offshore wind energy will become a very important part of the global energy internet in the future. After 20 years of wind power operation experience and the wind power technology improvement, the business and operation environment of offshore wind power development is becoming more and more mature. As the wind turbines installation site extends from land to offshore and deep-sea areas, “large” wind turbines have become the trend of offshore wind turbines development in the future. With the large-capacity offshore wind turbine prototype of many wind turbine manufacturers in China coming off the production line and successfully put into operation in China’s coastal areas, offshore wind turbines above 6MW began to be put into commercial operation. Therefore, it is urgent to carry out relevant tests and inspections to improve the operation and grid connection performance of the unit. It is of great significance to master the operation characteristics and control methods of offshore wind turbine, qualitatively and quantitatively analyze its impact on power grid, study offshore wind power detection methods and develop relevant detection devices. Due to the differences of offshore environment, operation control and the particularity of offshore power transmission and collection mode, the onshore wind power test and detection ability can not adapt to the new changes and requirements of offshore wind energy test and detection.
Properties of neutral mesons in a hot and magnetized quark matter: Size-dependent effects

In this work we investigate the combined finite-size and thermomagnetic effects on the properties of neutral mesons in a hot medium, in the context of the Nambu–Jona-Lasinio model. In particular, by using the mean-field approximation and the Schwinger proper time method in a toroidal topology with periodic and antiperiodic conditions, we evaluate the chiral phase transition, the critical coupling, the constituent quark mass and meson observables like the π and σ meson masses and pion decay constant under the change of the size, temperature and strength of external magnetic field. The results indicate that the observables are strongly affected by the conjoint effects of relevant variables and also by the periodicity of the boundary conditions chosen, and the net result will depend on the balance of these competing phenomena.
Abstractive Arabic Text Summarization Based on Deep Learning

Text summarization (TS) is considered one of the most difficult tasks in natural language processing (NLP). It is one of the most important challenges that stand against the modern computer system's capabilities with all its new improvement. Many papers and research studies address this task in literature but are being carried out in extractive summarization, and few of them are being carried out in abstractive summarization, especially in the Arabic language due to its complexity. In this paper, an abstractive Arabic text summarization system is proposed, based on a sequence-to-sequence model. This model works through two components, encoder and decoder. Our aim is to develop the sequence-to-sequence model using several deep artificial neural networks to investigate which of them achieves the best performance. Different layers of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) have been used to develop the encoder and the decoder. In addition, the global attention mechanism has been used because it provides better results than the local attention mechanism. Furthermore, AraBERT preprocess has been applied in the data preprocessing stage that helps the model to understand the Arabic words and achieves state-of-the-art results. Moreover, a comparison between the skip-gram and the continuous bag of words (CBOW) word2Vec word embedding models has been made. We have built these models using the Keras library and run-on Google Colab Jupiter notebook to run seamlessly. Finally, the proposed system is evaluated through ROUGE-1, ROUGE-2, ROUGE-L, and BLEU evaluation metrics. The experimental results show that three layers of BiLSTM hidden states at the encoder achieve the best performance. In addition, our proposed system outperforms the other latest research studies. Also, the results show that abstractive summarization models that use the skip-gram word2Vec model outperform the models that use the CBOW word2Vec model.
Adaptive Bézier Degree Reduction and Splitting for Computationally Efficient Motion Planning

As a parametric polynomial curve family, Bézier curves are widely used in safe and smooth motion design of intelligent robotic systems from flying drones to autonomous vehicles to robotic manipulators. In such motion planning settings, the critical features of high-order Bézier curves such as curve length, distance-to-collision, maximum curvature/velocity/acceleration are either numerically computed at a high computational cost or inexactly approximated by discrete samples. To address these issues, in this article we present a novel computationally efficient approach for adaptive approximation of high-order Bézier curves by multiple low-order Bézier segments at any desired level of accuracy that is specified in terms of a Bézier metric. Accordingly, we introduce a new Bézier degree reduction method, called <italic>parameterwise matching reduction</italic>, which approximates Bézier curves more accurately compared to the standard least squares and Taylor reduction methods. We also propose a new Bézier metric, called the <italic>maximum control-point distance</italic>, that can be computed analytically, has a strong equivalence relation with other existing Bézier metrics, and defines a geometric relative bound between Bézier curves. We provide extensive numerical evidence to demonstrate the effectiveness of our proposed Bézier approximation approach. As a rule of thumb, based on the degree-one matching reduction error, we conclude that an <inline-formula><tex-math notation="LaTeX">$ n^{\rm{th}} $</tex-math></inline-formula>-order Bézier curve can be accurately approximated by <inline-formula><tex-math notation="LaTeX">$3(n-1)$</tex-math></inline-formula> quadratic and <inline-formula><tex-math notation="LaTeX">$6(n-1)$</tex-math></inline-formula> linear Bézier segments, which is fundamental for Bézier discretization.
Markov decision processes with observation costs

We consider Markov decision processes where the state of the chain is only given at chosen observation times and of a cost. Optimal strategies involve the optimisation of observation times as well as the subsequent action values. We consider the finite horizon and discounted infinite horizon problems, as well as an extension with parameter uncertainty. By including the time elapsed from observations as part of the augmented Markov system, the value function satisfies a system of quasi-variational inequalities (QVIs). Such a class of QVIs can be seen as an extension to the interconnected obstacle problem. We prove a comparison principle for this class of QVIs, which implies uniqueness of solutions to our proposed problem. Penalty methods are then utilised to obtain arbitrarily accurate solutions. Finally, we perform numerical experiments on three applications which illustrate our framework.
HPCGen: Hierarchical K-Means Clustering and Level Based Principal Components for Scan Path Genaration

In this paper, we present a new approach for decomposing scan paths and its utility for generating new scan paths. For this purpose, we use the K-Means clustering procedure to the raw gaze data and subsequently iteratively to find more clusters in the found clusters. The found clusters are grouped for each level in the hierarchy, and the most important principal components are computed from the data contained in them. Using this tree hierarchy and the principal components, new scan paths can be generated that match the human behavior of the original data. We show that this generated data is very useful for generating new data for scan path classification but can also be used to generate fake scan paths. Code can be downloaded here https://atreus.informatik. uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/? p=%2FHPCGen&mode=list.
Is there a latent left ventricular dysfunction in hypertensive patients with preserved ejection fraction ?

SUMMARY Introduction: Early detection of left ventricular(LV) dysfunction may represent a clinical finding that would justify aggressive treatment aimed to reduce cardiovascular morbidity and mortality. Aim: To evaluate longitudinal contractility in patients with essential hypertension and preserved LV ejection fraction (EF), in an attempt to detect latent impairment of LV systolic function. Methods: Prospective case-control study, carried out on 121 (67 male/54 female) hypertensive patients (HTN group) with preserved EF and without any symptoms of heart failure and 39 age- and gender-matched healthy subjects as a control group. Conventional echocar¬diographic study, as well as 2D Longitudinal strain imaging by 2D-speckle tracking echocardiography (2D-STE), were performed. Results: Mean age of patients was 60,48 ± 10.5 years old. The LVend-diastolic diameter and LVEF were comparable between the two groups. Hypertensive patients had greater septal thickness, left ventricular mass,and maximum left atrium volume (p respectively at 0.02; 0.04; and 0.01). Only 20 patients (16.5%) had left ventricular hypertrophy (LVH). The architecture of LV was normal in 57.8 % (n=70) patients. A statistically significant difference between the two groups was found for all diastolic function parameters except Em /Ea ratio and DTEm. In comparison with normal con¬trols, GLS was significantly attenuated in patients with HTN (-17.69 ± 4.06 % versus -22.70 ± 5.02% in controls (p=0.000) and 67 (55.4%) hypertensive patients had a GLS inférieur à -20% (in absolute value). The decrease of GLS was more marked in the hypertensive group with left ventricular hypertrophy. Conclusion: The results of our study confirmed that GLS is a sensitive biomarker of subclinical myocardial dysfunction in hypertensive patients, this suggests that identifying patients at higher risk for heart failure and earlier inter¬vention may be beneficial.
Metropolis Augmented Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is a powerful Markov Chain Monte Carlo (MCMC) method for sampling from complex high-dimensional continuous distributions. However, in many situations it is necessary or desirable to combine HMC with other Metropolis-Hastings (MH) samplers. The common HMC-within-Gibbs strategy implies a trade-off between long HMC trajectories and more frequent other MH updates. Addressing this trade-off has been the focus of several recent works. In this paper we propose Metropolis Augmented Hamiltonian Monte Carlo (MAHMC), an HMC variant that allows MH updates within HMC and eliminates this trade-off. Experiments on two representative examples demonstrate MAHMC's efficiency and ease of use when compared with within-Gibbs alternatives.
HumanIBR: High Quality Image-based Rendering of Challenging Human Performers using Sparse Views

—In this paper, we introduce HumanIBR, a method that addresses the challenge of novel view rendering of human performers that wear clothes with complex patterns using a sparse set of camera views. Some recent works have achieved remarkable rendering quality on humans that wear pure clothes using sparse views, but if the clothes have complex color patterns, the rendering quality is still very low. To this end, the proposed HumanIBR uses a human reconstruction net with pixel-aligned spatial transformer and a render net that uses geometry-guided pixel-wise feature integration to achieve to goal of high quality human reconstruction and rendering. The designed pixel-aligned spatial transformer calculates the correlations between the input views, producing human reconstruction results with high-frequency details presented in the input views. Based on the reconstruction, the geometry-guided pixel-wise visibility reasoning provides a guidance for multi-view feature integration, enabling the render net to render high quality images on novel views. Unlike previous neural rendering works that always need to train or ﬁne-tune a separate network for each scene or human, our method is a general framework that is able to generalize to novel humans. Experiments show that our approach outperforms all the prior general or human-speciﬁc works on both synthetic data and real-world data.