Miracle-dz/UniFAR-FTUs huggingface dataset:
Features of the Structure of the Material Formed from AK4-1 Powder Using Laser Energy

The paper presents the results of studying the microstructure of samples obtained with laser processing of AK4-1 aluminum alloy powder. It has been found that increasing the initial temperature of the substrate to 100.150 °C allows to use this powder material in directed energy deposition (DED) additive process to create stiffening elements on the surface of thin-walled aluminum parts. When the specified temperature condition is satisfied, the crystallization rate decreases, allowing to obtain samples with almost no internal cracks. The results of comparing the microstructure and the microhardness of commercial AK4-1 wrought alloy and the samples obtained with DED process are presented. The structure of the samples prepared with laser cladding is more disperse in comparison with wrought alloy. The microhardness of the sample prepared at pre-heated substrate is comparable with wrought alloy. The conceptual architecture of the decision support software for laser powder cladding processes is presented. Its information and software components are briefly described.
Larger Doesn't Mean Longer: Neither Body Size Nor Seed Size Affect the Gut Retention Times of Aldabra Giant Tortoises

Abstract Seed dispersal through endozoochory is a critical ecosystem function worldwide. Seed gut retention time (GRT; the duration that seeds are retained in the digestive tract) is an important part of the qualitative component of the seed dispersal effectiveness framework. GRT is a major determinant of when and how far away seeds are dispersed, aiding seeds in escaping predation in space as well as in time. In this study, we examined whether the size of the disperser and/or the size of the ingested seeds affect the GRT in Aldabra Giant Tortoises (Aldabrachelys gigantea) on Aldabra Atoll, Seychelles. We selected tortoises of varying body mass (mean ± 1 SD = 48.6 ± 27.2 kg, range = 0.6–104.0 kg) and fed them different-sized artificial seeds (plastic beads; 2, 4.5, and 10 mm diameter). Tortoises defecated the first beads a mean of 12.0 ± 2.7 d after ingestion, and the last beads 20.4 ± 6.0 d after ingestion. Mean GRT was 14.6 ± 3.7 d. We show that neither tortoise body size nor bead size had an effect on the patterns and time of defecation. We discuss the relevance of our result for seed dispersal and for rewilding projects that use Aldabra Giant Tortoises as substitute species for extinct giant tortoises on other oceanic islands.
Atomistic Level Molecular Dynamics Analysis and its Integrity in the Interim of Irradiation

In this work, molecular dynamics (MD) simulation was utilized in relation to access the thermal conductivity of UO2, PuO2 and (U, Pu)O2 in temperature range of 500–3000 K. Diffusion study on mixed oxide (MOX) was also performed to assess the effect of radiation damage by heavy ions at burnup temperatures. Analysis of the lattice thermal conductivity of irradiated MOX to its microstructure was carried out to enhance the irradiation defects with how high burnup hinders fuel properties and its pellet-cladding interaction. Fission gas diffusion as determined was mainly modelled by main diffusion coefficient. Degradation of diffusivity is predicted in MOX as composition deviate from the pure end members. The concentration of residual anion defects is considerably higher than that of cations in all oxides. Depending on the diffusion behavior of the fuel lattice, there was decrease in the ratio of anion to cation defects with increasing temperature. Besides, the modern mixed oxide fuel releases fission gas compared to that of UO2 fuel at moderate burnups.
A New Nurse Frog (Allobates, Aromobatidae) with a Cricket-Like Advertisement Call from Eastern Amazonia

Abstract Integrative analyses, long-term studies, and access to remote areas in Amazonia have led to new hypotheses and increased resolution of the systematics and taxonomy of the small nurse frog genus Allobates (family Aromobatidae). During anuran sampling in the Middle Tapajós River region, state of Pará, Brazil, we collected data on a new cryptically colored species of Allobates with a cricket-like advertisement call. Here, we name and describe this new species, following an integration of phenotypic, ecological, and molecular analyses. The results of a phylogenetic analysis based on mitochondrial and nuclear DNA placed the new species as the sister taxon of Allobates grillisimilis. Genetic pdistances between the two sister taxa at the 16S region of the mitochondrial DNA ranged from 7% to 9%. A sister-species relationship between the new species and A. grillisimilis is also supported by phenotypic evidence. Adults of the new species are small (snout–vent length 15.2–16.8 mm in males and 16.5–17.7 mm in females), with a dorsum uniformly tan to reddish brown, a dark brown lateral stripe and a white ventrolateral stripe, arms pale tan brown and legs greyish-brown, and venter yellowish colored, with variable extension and shades of yellow, paler on the throat. The advertisement call is a trill with a mean peak frequency of 5830.2 Hz, arranged in series of short, closely spaced, pulses (mean of 24.3 pulses/s), and followed by silent intervals of variable duration. We discuss the putative drivers generating and maintaining the distinctiveness between the new species and the allopatrically distributed sister taxon, and on threats to the persistence of the new species. With this species description, the Middle Tapajós River region is consolidated as having one of the highest alpha diversities for the genus Allobates in Amazonia.
A New Species of the Genus Adelophryne (Anura: Eleutherodactylidae: Phyzelaphryninae) from the Atlantic Forest of Northeastern Brazil

Abstract Species in the Adelophryne genus consist of diminutive, agile, and secretive frogs that inhabit forest leaf litter and have direct development. Species richness within Adelophryne was previously underestimated, and several new species have recently been described. Here, we describe a new species of Adelophryne from the Atlantic Forest in the Brazilian states of Alagoas, Pernambuco, and Paraíba. The new species is characterized by its small body size, small and distinct tympanum, toes with subarticular tubercles, fingers mucronate, three phalanges in the Finger IV, and an advertisement call composed of one single pulsed note with a high dominant frequency. The new species is phylogenetically related to other Adelophryne species of the Northern Atlantic Forest Clade and is endemic to the “Pernambuco Endemism Center” in the Atlantic Forest biodiversity hotspot.
Modified Gypsum Binder for Plastering Systems

A natural gypsum binder or its analogs, which are the products of local raw materials recycling or by-products of other industries, is used as a basic component in the composition of interior plastering mixtures. Plaster coatings based on a gypsum binder have two main features. The first is the ability to control room humidity and absorb harmful emissions. The second is the ability to implement the barrier protection function in case of fire.Improvement of plastering works quality and optimization of their cost is related to the development of modified gypsum-based compositions, the justified use of waste, and local building materials, which include a clay-gypsum binder. Clay gypsum as a natural material of sedimentary origin is widespread both in Russia and in post-Soviet countries. In terms of energy intensity and manufacturability, raw materials recycling into clay-gypsum binder does not differ from the conditions of traditional processing of natural gypsum. Compared with lime or cement mortars, mortars based on clay-gypsum binders have greater elasticity and plasticity. Such properties as convenient masonry and thixotropy as well as the interval for maintaining the viability of the mixture are related to application technology and regulated by the insertion of modifying additives, which formulation was the aim of the research. The research results are presented in this article. Studies have shown that varying the recipe parameters allows us to control both the strength and operating characteristics of clay-based mixtures, as well as their application technology.
An optimized tensor completion library for multiple GPUs

Tensor computations are gaining wide adoption in big data analysis and artificial intelligence. Among them, tensor completion is used to predict the missing or unobserved value in tensors. The decomposition-based tensor completion algorithms have attracted significant research attention since they exhibit better parallelization and scalability. However, existing optimization techniques for tensor completion cannot sustain the increasing demand for applying tensor completion on ever larger tensor data. To address the above limitations, we develop the first tensor completion library cuTC on multiple Graphics Processing Units (GPUs) with three widely used optimization algorithms such as alternating least squares (ALS), stochastic gradient descent (SGD) and coordinate descent (CCD+). We propose a novel TB-COO format that leverages warp shuffle and shared memory on GPU to enable efficient reduction. In addition, we adopt the auto-tuning method to determine the optimal parameters for better convergence and performance. We compare cuTC with state-of-the-art tensor completion libraries on real-world datasets, and the results show cuTC achieves significant speedup with similar or even better accuracy.
Exposing Bot Attacks Using Machine Learning and Flow Level Analysis

Botnets represent a major threat to Internet security that have continuously developed in scale and complexity. Command-and-control servers (C&C) send commands to bots that execute and perform these commands, thereby implementing attacks such as distributed denial-of-service (DDoS), spam campaigns, or the scanning of compromised hosts. The detection of volumetric attacks in large and complex networks requires an efficient mechanism. Botnet behavior should be analyzed in order to save the network from attack, and preventive measures should be implemented in time. Anomalous botnet tracking strategies are more efficient than signature-based ones, since botnet detection methods rely on anomalies and do not need pre-constructed botnet signatures, therefore they can detect new or unidentified botnets. We use Netflow and machine learning algorithms in this paper to also improve the detection process for intrusion detection algorithms with a novel dataset. We implemented a number of algorithms in our lightweight model to show that Random Forests get the highest accuracy for the algorithms used.
Graffiti and government in smart cities: a Deep Learning approach applied to Medellín City, Colombia

Graffiti is an element of graphic expression that manifests different states of the human being. However, for many governments worldwide, it has been an element of discord between them and the communities that express themselves through graffitis. This article proposes identifying graffiti and concentration zones through Computer Vision and object detection and localization to support public policy management in smart cities. ASUM-DM methodology is used to achieve the aim. Initially, the current problems faced by municipal governments in the management of public graffiti policy are identified. Then available datasets of images from Google Street View (GSV) and other acquired datasets are identified for the case study carried out in the city of Medellín (Colombia) and border municipalities. A training dataset of 1,395 images and a production dataset of 71,100 panoramas is placed on strictly using the experimental method of the division of training data, validation, and a production sample, to make a correct estimation of the generalization error. As a result of the training process, we obtained an Average Precision of 69,14%, which presented a high precision Tag of 89.23%, and low precision of 59.13% in Mural. Finally, it is possible to build heat maps of graffiti concentration areas that could guide rulers to create or improve public policies related to graffiti expression.
Analysis of vessel abnormal behavior detection

In this paper, we summarize the literature on vessel abnormal behavior detection in recent ten years, and analyze the development trend of related research from time series with charts and tables, and further sort out and summarize the research on vessel abnormal behavior detection combined with the classification method dominated by research results. Finally, we get the problems of vessel abnormal behavior detection in four aspects: comprehensiveness, big data processing, algorithm improvement and specific environment analysis.
Optimal model of optical parameter extraction for material rendering

Hyper-real rendering appropriate for movie-like gameplay is being actively studied in the graphics field. To express human skin or various materials realistically, shading technology that can reflect the physical features of a material is needed. The core factor in rendering is optical parameters, and the accuracy of these parameters based on the material's optical features determines the rendering quality. The parameters required for rendering are extracted with a spectrographic optical shooting device and by curve fitting. Existing processes for parameter extraction require significant time. To overcome this disadvantage, we produced an HDRI (high-dynamic range image) generating program with exposure fusion algorithm. The number of low-dynamic range images for an HDRI was optimised through an experiment. In addition, the amount of sample data, which mostly affect extraction time in automated systems, can be optimised to reduce time loss during the photo-shoot, fusion and extraction processes.
Intelligent learning systems for LLL courses: Intelligent learning systems for LLL courses

The proposed model for organizing blended and distance learning involves the creation of an individual learning path, which makes it flexible. The learning model is represented using an ontological model, and the decision rules for the model are logical rules. This training model is used to train teachers in digital literacy and distance learning in the context of Covid 19
3D Tensor Auto-encoder with Application to Video Compression

Auto-encoder has been widely used to compress high-dimensional data such as the images and videos. However, the traditional auto-encoder network needs to store a large number of parameters. Namely, when the input data is of dimension n, the number of parameters in an auto-encoder is in general O(n). In this article, we introduce a network structure called 3D Tensor Auto-Encoder (3DTAE). Unlike the traditional auto-encoder, in which a video is represented as a vector, our 3DTAE considers videos as 3D tensors to directly pass tensor objects through the network. The weights of each layer are represented by three small matrices, and thus the number of parameters in 3DTAE is just O(n1/3). The compact nature of 3DTAE fits well the needs of video compression. Given an ensemble of high-dimensional videos, we represent them as 3DTAE networks plus some small core tensors, and we further quantize the network parameters and the core tensors to get the final compressed data. Experimental results verify the efficiency of 3DTAE.
Detection of Contaminated Hazelnuts under UV Illumination

Hazelnut (Corylus avellana L.) is one of the most important crops in the world. Due to its high nutritional composition and enhanced flavors, it is used in numerous confectioneries, baking, and chocolate industries. During various cultivation, harvesting, and storage phases, hazelnuts can be prone to bacterial and fungal infections which damage their nutritional values as well as result in bad odor and unpleasant taste. This work proposes a solution to detect rotten hazelnuts by means of Ultraviolet (UV) light. In order to uniformly illuminate hazelnut samples and to capture high-quality images, an axial illuminator is specially modeled and printed using a 3D-printer. The sample images are captured at 450 nm and 750 nm. The healthy and rotten hazelnuts behave differently in the presence of UV light, such that the healthy parts produce fluorescence after absorbing the UV light and appear brighter, whereas the rotten hazelnut samples do not produce any fluorescence and are therefore darker in the captured images. The two images are then combined by using image division operation with suitable scaling factors. The resulting gray-scale images are converted into binary images using image thresholding. These binary images represent the damaged or rotten hazelnuts as black and the healthy hazelnuts are white in color. The percentages of black and white pixels determine whether a hazelnut sample belongs to the healthy or the rotten class.
Intelligent Highway Speed Monitoring UAV System Based on Deep Learning

At present, with economic development, traffic accidents occur frequently, and more than one-third of traffic accidents are caused by speeding. Current highway speed measurement devices have disadvantages such as high cost, inability to move, and low speed measurement accuracy. For these reasons, we propose an intelligent monitoring drone system for highway speed measurement based on deep learning. The system uses drones for monitoring, so the system is low-cost and flexible. First, the system's camera captures and recognizes vehicle movement and license plate information. Second, we propose algorithms based on the corners of the lane line speed measurement and the speed measurement based on the homography matrix to calculate the vehicle speed, and at the same time detect and recognize the license plate. Third, we propose to use ensemble learning methods to improve the accuracy of vehicle speed measurement, and finally obtain vehicle speed and speeding license plates. Experimental results prove that the proposed highway speed measurement UAV system can accurately identify and measure vehicle speed and record speeding license plates.
Fake News Detection Using Machine Learning Methods

The paper is about the detection of unauthenticated news using Machine-learning methods with different algorithms. There is lot of scope to check the reality of the news received from various sources like websites, blogs, e-content. To identify the fake news, there is a need of some application in real time. Many methods were proposed earlier to observe fake news such as style-based, propagation-based and user-based. Automatic fake news detection application can be generated using natural language processing, information retrieval techniques, as well as graph theory. Language modeling is used to predict the missing or next word in a sentence based on the context. It is believed that mainstream media platforms are publishing fake news to grasp the attention of readers; most likely, it is done to increase the number of visitors on that particular page so that with an increasing number of visitors the page could claim more advertisement. This paper proposes an efficient method to detect fake news with better accuracy by using the available data set to detect the news is FAKE or REAL. Various methods are used for collecting the data and the data mining techniques are applied to clean and visualize it. Data mining helps to differentiate between the qualities of data depending upon its properties. The performance of detecting news only from the body of news is not sufficient but also social engagements should be considered. The objective of the work is to provide end-users with a robust solution so that they can figure out phishy and misguiding information. This technique combines the title and the body of the news to predict fake news more efficiently. The application is concerned with finding a result that could be used to identify fake news to help users.
Horizontal Privacy-Preserving Linear Regression Which is Highly Efficient for Dataset of Low Dimension

Linear regression is a widely used machine learning model for applications such as personalized health-care prediction, recommendation systems, and policy making etc. Nowadays one important trend of applying this model (also others) is privacy-preserving linear regression, in which multiple parties, each possessing a part of dataset, jointly perform the learning process, while paying a specific attention to the goal of preserving privacy of their data. Consequently some works on how to achieve this goal with various properties appear in recent years. In this paper, we present a new privacy-preserving linear regression protocol for the scenario where dataset is distributed horizontally, which works highly efficiently in particular when training dataset is of low dimension. Our protocol uses two non-colluding servers, in which multiple data providers share their private data, i.e. a dxd matrix where d denotes the dimension of dataset, into two shares and send shares to the two servers respectively which then jointly perform the training process securely. Our technical novelties are as follows. We remark that in the method of solving linear systems (SLS), one mainstream method for linear regression (while the other one is stochastic gradient descent (SGD)), the time complexity only depends on the dimension, regardless of the number of samples. Note that known works using SLS employ garbled circuits for entire linear systems and thus use heavy computation. We implement SLS via the share-computation method which is known for securely implementing SGD and has not been applied to SLS to our knowledge, and thus inherit the advantages from them both (note that SLS admits fast computation when dataset is of low dimension, and the share-computation method is faster than the method of garbled circuits for entire linear systems). In the share-computation for SLS we propose a hybrid method, combining garbled circuits and secret sharing, to realize a secure and round-efficient division for fixed-point number (8+2θ rounds, where θ is a small number of iterations and is set to 5 in our experiment). We then use the division protocol to implement our new protocol for linear regression. As a consequence, our protocol is highly efficient for dataset of low dimension. We implement our protocol in C++ and the experiments show that our protocol is much more efficient than the state of the art implementations for privacy-preserving linear regression for dataset of low dimension.
MACHINE LEARNING FRAMEWORK FOR COVID-19 DIAGNOSIS

With the alarming global health crisis and pandemic, the entire medical industry and every human in this world are desperately looking for new technologies and solutions to monitor and contain the spread of this COVID-19 virus through early detection of its presence among infected patients. The early diagnosis of COVID-19 is hence critical for prevention and limiting this pandemic before it engulfs the humanity. With early diagnosis, the patient may be suggested for self-isolation (or) quarantine under medical supervision. Early detection of COVID-19 can save the patient and minimize the risk of falling prey to CoviD-19. Machine learning, a subset field of Artificial Intelligence can provide a viable solution for early diagnosis of disease and facilitate continuous monitoring of infected patients. AI based approaches can provide a view of the degree of disease severity. In general, Artificial intelligence (AI) could be a better technique for quantitative evaluation of the disease to obtain fruitful results. This paper throws light on the emerging need for AI powered solutions to foster early diagnosis of COVID-19 and suggest an ML based health monitoring framework for diagnosis of infected patients.