Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of the Infrared Thermography and Unmanned Ground Vehicle for Rescue Action Support in Underground Mine - The AMICOS Project

Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such critical situations, rescue actions may require advanced technologies as autonomous mobile robot, various sensory system including gas detector, infrared thermography, image acquisition, advanced analytics, etc. In the paper, we describe several scenarios related to rescue action in underground mines with the assumption that searching for sufferers should be done considering potential hazards such as seismic, gas, high temperature, etc. Thus, possibilities of rescue team activities in such areas may be highly risky. This work reports the results of testing of a UGV robotic system in an underground mine developed in the frame of the AMICOS project. The system consists of UGV with a sensory system and image processing module that are based on an adaptation of You Only Look Once (YOLO) and Histogram of Oriented Gradients (HOG) algorithms. The experiment was very successful; human detection efficiency was very promising. Future work will be related to test the AMICOS technology in deep copper ore mines.
Feature Line Embedding Based on Support Vector Machine for Hyperspectral Image Classification

In this paper, a novel feature line embedding (FLE) algorithm based on support vector machine (SVM), referred to as SVMFLE, is proposed for dimension reduction (DR) and for improving the performance of the generative adversarial network (GAN) in hyperspectral image (HSI) classification. The GAN has successfully shown high discriminative capability in many applications. However, owing to the traditional linear-based principal component analysis (PCA) the pre-processing step in the GAN cannot effectively obtain nonlinear information; to overcome this problem, feature line embedding based on support vector machine (SVMFLE) was proposed. The proposed SVMFLE DR scheme is implemented through two stages. In the first scatter matrix calculation stage, FLE within-class scatter matrix, FLE between-scatter matrix, and support vector-based FLE between-class scatter matrix are obtained. Then in the second weight determination stage, the training sample dispersion indices versus the weight of SVM-based FLE between-class matrix are calculated to determine the best weight between-scatter matrices and obtain the final transformation matrix. Since the reduced feature space obtained by the SVMFLE scheme is much more representative and discriminative than that obtained using conventional schemes, the performance of the GAN in HSI classification is higher. The effectiveness of the proposed SVMFLE scheme with GAN or nearest neighbor (NN) classifiers was evaluated by comparing them with state-of-the-art methods and using three benchmark datasets. According to the experimental results, the performance of the proposed SVMFLE scheme with GAN or NN classifiers was higher than that of the state-of-the-art schemes in three performance indices. Accuracies of 96.3%, 89.2%, and 87.0% were obtained for the Salinas, Pavia University, and Indian Pines Site datasets, respectively. Similarly, this scheme with the NN classifier also achieves 89.8%, 86.0%, and 76.2% accuracy rates for these three datasets.
Estimation of the Grassland Aboveground Biomass of the Inner Mongolia Plateau Using the Simulated Spectra of Sentinel-2 Images

An accurate assessment of the grassland aboveground biomass (AGB) is important for analyzing terrestrial ecosystem structures and functions, estimating grassland primary productivity, and monitoring climate change and carbon/nitrogen circulation on a global scale. Multispectral satellites with wide-width advantages, such as Sentinel-2, have become the inevitable choice for the large-scale monitoring of grassland biomass on regional and global scales. However, the spectral resolution of multispectral satellites is generally low, which limits the inversion accuracy of grassland AGB and restricts further application in large-scale grassland monitoring. For this reason, a satellite-scale simulated spectra method was proposed to enhance the spectral information of the Sentinel-2 data, and a simulated spectrum (SS) was constructed using this algorithm. Then, the raw spectrum (RS) of Sentinel-2 and the SS were used as data sources to calculate the vegetation indices (RS-VIs and SS-VIs, which represent vegetation indices calculated using RS and SS data, respectively), and the multi-granularity spectral segmentation algorithm (MGSS) was employed to extract spectral segmentation features (RS-SF and SS-SF, which represent segmentation features extracted by RS and SS data, respectively). Following this, these spectral features (RS-SF, SS-SF, RS-VIs, and SS-VIs) were used to estimate AGB by partial least-squares regression (PLSR) and multiple stepwise regression (MSR) models. Finally, the spatial distribution law and the reasons for the latitude zone of the Inner Mongolia Plateau were analyzed, based on precipitation, the average temperature, topography, etc. The conclusions are as follows. Firstly, the SS has more spectral information and its sensitivity to biomass is higher than the RS of Sentinel-2 in some bands, and the correlation between the SS-VIs and biomass is higher than that of the RS-VIs. Secondly, among the spectral features, the most accurate AGB estimation was obtained by SS-SF, which gave R2 = 0.95. The root mean square error (RMSE) was 10.86 g/m2 and the estimate accuracy (EA) was 82.84% in the MSR model. Additionally, RMSE = 10.89 g/m2 and EA = 82.78% in the PLSR model. Compared with the traditional estimation methods using RS and VI, R2 was increased by at least 0.2, RMSE was reduced by at least 14.08 g/m2, and EA was increased by 22.26%. Therefore, the simulated spectra method can help improve the estimation accuracy of AGB, and a new idea about regional and global large-scale biomass acquisition is provided.
Supramolecular gels based on monopyrrolotetrathiafulvalene and its TCNQ charge-transfer complex

A novel series of monopyrrolotetrathiafulvalene (monopyrrolo-TTF)-based low molecular-mass organic gelators (LMOGs) were synthesized and characterized. Electrochemical investigations exhibited two reversible one-electron redox couples. The gelators could gelate some saturated hydrocarbons and alcohols. The FE-SEM images of xerogels revealed the characteristic gelation morphologies of microporous structures. The gelators reacted with tetracyano-p-quinodimethane (TCNQ) to form the charge-transfer (CT) complexes and the binary organogels, and the morphologies of binary gels changed to regular network and fiber in cyclohexane and n-hexane, respectively. XRD studies suggest that both the gelator and the CT complex maintain the lamellar molecular-packing mode in the organogel phase. The gel–sol transition of the organogel and the CT complex gel could be stimulated by fluorine and chlorine ions.
Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon

A reliable estimation and monitoring of tree canopy cover or shade distribution is essential for a sustainable cocoa production via agroforestry systems. Remote sensing (RS) data offer great potential in retrieving and monitoring vegetation status at landscape scales. However, parallel advancements in image processing and analysis are required to appropriately use such data for different targeted applications. This study assessed the potential of Sentinel-1A (S-1A) C-band synthetic aperture radar (SAR) backscatter in estimating canopy cover variability in cocoa agroforestry landscapes. We investigated two landscapes, in Center and South Cameroon, which differ in predominant vegetation: forest-savannah transition and forest landscape, respectively. We estimated canopy cover using in-situ digital hemispherical photographs (DHPs) measures of gap fraction, verified the relationship with SAR backscatter intensity and assessed predictions based on three machine learning approaches: multivariate bootstrap regression, neural networks regression, and random forest regression. Our results showed that about 30% of the variance in canopy gap fraction in the cocoa production landscapes was shared by the used SAR backscatter parameters: a combination of S-1A backscatter intensity, backscatter coefficients, difference, cross ratios, and normalized ratios. Based on the model predictions, the VV (co-polarization) backscatter showed high importance in estimating canopy gap fraction; the VH (cross-polarized) backscatter was less sensitive to the estimated canopy gap. We observed that a combination of different backscatter variables was more reliable at predicting the canopy gap variability in the considered type of vegetation in this study—agroforests. Semi-variogram analysis of canopy gap fraction at the landscape scale revealed higher spatial clustering of canopy gap, based on spatial correlation, at a distance range of 18.95 m in the vegetation transition landscape, compared to a 51.12 m spatial correlation range in the forest landscape. We provide new insight on the spatial variability of canopy gaps in the cocoa landscapes which may be essential for predicting impacts of changing and extreme (drought) weather conditions on farm management and productivity. Our results contribute a proof-of-concept in using current and future SAR images to support management tools or strategies on tree inventorying and decisions regarding incentives for shade tree retention and planting in cocoa landscapes.
Thermal Infrared Radiation and Laser Ultrasound for Deformation and Water Saturation Effects Testing in Limestone

During the operation of engineering structures made of natural stone, for industrial and civil purposes, an important parameter in monitoring their technical condition is the assessment of their reliability and safety under the influence of various external influences. In this case, high-quality monitoring of the stress–strain state of natural stone structures, its physical, mechanical and filtration properties, as well as internal structural features is necessary to study the possibility of replacing individual elements of objects that have lost their original characteristics. To assess the state of geomaterials, this article proposes using a complex of introscopic methods, including infrared radiometry and laser-ultrasound structuroscopy. An important aspect is the calculation based on the Green–Christoffel equation of the velocity of a quasi-longitudinal wave in limestone consisting of densely packed, chaotically oriented calcite grains with a small quartz content. For the first time, using laser-ultrasonic structuroscopy and standard methods for determining open porosity, both total and closed porosity were determined. This allowed us to find the values of specific heat capacities of dry and water-saturated samples. The obtained values are used to find the ratio of changes in the temperature of dry and water-saturated samples at the same stress values. The results obtained demonstrate the need to take into account changes in the intensity of thermal radiation of limestone with different moisture content under conditions of uniaxial compression, when identifying changes in the stress state of elements of stone structures in real conditions.
Projected Wirtinger Gradient Descent for Digital Waves Reconstruction

This work attempts to recover digital signals from a few stochastic samples in time domain. The target signal is the linear combination of one-dimensional complex sine components with R different but continuous frequencies. These frequencies control the continuous values in the domain of normalized frequency [0, 1), contrary to the previous research into compressed sensing. To recover the target signal, the problem was transformed into the completion of a low-rank structured matrix, drawing on the linear property of the Hankel matrix. Based on the completion of the structured matrix, the authors put forward a feasible-point algorithm, analyzed its convergence, and speeded up the convergence with the fast iterative shrinkage-thresholding (FIST) algorithm. The initial algorithm and the speed up strategy were proved effective through repeated numerical simulations. The research results shed new lights on the signal recovery in various fields.
Dynamic Control of DFIG used in Wind Power Production, based on PI regulator

This paper proposes the modeling and the control of the DFIG (Doubly-Fed Induction Machine) used in the WECS (Wind Energy Conversion System). Two proposed PI (Proportional Integral) controller are used in this paper. The first one is used to control the RSC (Rotor Side Converter) to extract the maximum power from the WT (Wind Turbine) while regulating the rotor currents. The second one is applied to the GSC (Grid Side Converter) to keep the continuous DC bus voltage constant. We imposed the wind speed into the DFIG to see the difference between the hyper-synchronous and hypo-synchronous modes. To validate the effectiveness of the proposed control, the system was modeled and simulated by using the Matlab/Simulink software environment.
Preictal and Interictal Recognition for Epileptic Seizure Prediction Using Pre-trained 2D-CNN Models

Epilepsy is a neurological disease affecting almost 1% of world population. Predicting a possible seizure will make a significant contribution to improving the quality of life of patients suffering from this disease. One of the most important steps in seizure prediction studies is the preictal activity recognition stage. In many previous studies, the preictal state was determined to end at the onset of the seizure, which makes it difficult for the physician to intervene in the patient in a possible seizure. In the proposed method, unlike previous studies, the preictal state was determined as the 30-minute interval ending 30 minutes before the onset of an epileptic seizure. The method consisted of three stages; (I) preictal and interictal activities were divided into five-second segments, (ii) the separated signals were converted into spectrograms, and (iii) the spectrogram images were classified using three different pre-trained CNN models (VGG19, ResNet, DenseNet) and the results were compared among these models. Classification was performed separately using the predetermined four EEG channels for 20 cases in the CHB-MIT dataset. The best classification accuracy value in preictal/interictal discrimination (91.05%) was obtained on channel 8 (P3-O1). An important contribution of this study was that the proposed approach provided important information about the preictal and interictal discrimination of the section 30 minutes before the onset of seizures. In addition, by examining the four channels separately, channel-based information on preictal/interictal discrimination was also obtained. Based on these results, we consider that the proposed method will bring a different perspective to seizure prediction studies.
Group Decision Model for Logistic Performance Analysis: a parallel between contracting and outsourced companies

Business logistics have an undeniable strategic importance and organizations can reach new markets and improve service levels for their customers by outsourcing. To maximize the benefits of the relationship between a shipper (i.e. logistics buyer) and a logistics provider, an alignment of goals and objectives is required. Thus, this paper developed a model that connects the perceptions of both parties. The model was based on the trade-off matrix, the priority matrix, and the importance-performance analysis matrix. A case study was adopted, considering two companies located in Caruaru-PE, Brazil. This case study highlights the divergent perceptions between the companies studied and how the proposed methodology can help in directing actions that create synergy in the supply chain.
ACOMTA: An Ant Colony Optimisation based Multi-Task Assignment Algorithm for Reverse Auction based Mobile Crowdsensing

Mobile Crowdsensing (MCS) systems take advantage of the ubiquity and sensing power of smartphones in data gathering. Reverse auction is a popular incentive mechanism framework for MCS wherein, the participants can determine their expected rewards for their contributions. In this paper, for the first time, we consider a multi-task location-dependent reverse auction based MCS setting wherein, each task requires a specific amount of contribution to be fulfilled, participants may need to move to the task locations in order to participate in them, and the goal is to assign each participant to at most one task in a way such that the cumulative contribution of the fulfilled tasks are maximised while not exceeding a limited budget. We show that this is an NP-hard optimization problem and propose Ant Colony Optimisation-based Multi-Task Assignment (ACOMTA) as an approximation algorithm for it. We uncover an issue with the basic instantiation of ACO and propose an approach called Valid Random Path Generator (VRPG) to avoid lack of or premature convergence of the algorithm. Through extensive experiments, we show that the proposed algorithm outperforms a greedy approach as well as a random-based solution.
Malware Detection Techniques: A Survey

Malware is derived from malicious software which mitigate to attacks on the computer systems and collecting private data. The survey is available in huge evidences to suggest its impact in global losses. Malware detectors are basic tools to protect from the same malware attacks. Therefore, it is important to require study on malware detection techniques, to avoid and identify the type of malware attacked on systems. In this manuscript, a survey report is available to defend against malware attacks and analysis techniques. There are many malware detection techniques, such as signature and anomaly detection techniques with an idea of comparison and decision making about its strengths. This provides as a user reference to the end user for likely detailed information.
Enzymatically Synthesized Conducting Polyaniline

A novel strategy for the enzymatic synthesis of a water-soluble, conducting polyaniline (PANI)/sulfonated polystyrene (SPS) complex is presented. The enzyme horseradish peroxidase (HRP) is used to polymerize aniline in the presence of a polyanionic template, sulfonated polystyrene. The synthesis is simple, and the conditions are mild in that the polymerization may be carried out in a 4.3 pH buffered aqueous solution, with a stoichiometric amount of hydrogen peroxide and a catalytic amount of enzyme. UV−visible absorption, FTIR, GPC, elemental analysis, and conductivity measurements all confirm that the electroactive form of PANI, similar to that which is traditionally chemically synthesized, is formed and complexed to the SPS. The reversible redox activity of the polyaniline displays a unique hysteresis loop with pH change. Cyclic voltammetry studies show only one set of redox peaks over the potential range of −0.2 to 1.2V, which suggests that the PANI/SPS complex is oxidatively more stable. The conductiv.
E2bird: Enhanced Elastic Batch for Improving Responsiveness and Throughput of Deep Learning Services

We aim to tackle existing problems about deep learning serving on GPUs in the view of the system. GPUs have been widely adopted to serve online deep learning-based services that have stringent QoS(Quality-of-Service) requirements. However, emerging deep learning serving systems often result in poor responsiveness and low throughput of the inferences that damage user experience and increase the number of GPUs required to host an online service. Our investigation shows that the poor batching operation and the lack of data transfer-computation overlap are the root causes of the poor responsiveness and low throughput. To this end, we propose E<inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="chen-ieq1-3047638.gif"/></alternatives></inline-formula>bird, a deep learning serving system that is comprised of a GPU-resident memory pool, a multi-granularity inference engine, and an elastic batch scheduler. The memory pool eliminates the unnecessary waiting of the batching operation and enables data transfer-computation overlap. The inference engine enables concurrent execution of different batches, improving the GPU resource utilization. The batch scheduler organizes inferences elastically to guarantee the QoS. Our experimental results on an Nvidia Titan RTX GPU show that E<inline-formula><tex-math notation="LaTeX">$^2$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="chen-ieq2-3047638.gif"/></alternatives></inline-formula>bird reduces the response latency of inferences by up to 82.4 percent and improves the throughput by up to 62.8 percent while guaranteeing the QoS target compared with TensorFlow Serving.
Towards an Intelligent Decision Making of Ti-based Powders Selection for Medical Manufacturing

Decision making process is an important task in many industries. The design of the intelligent decision support systems is of particular importance in areas where the final decision is difficult to make by the operator, which significantly affects both material costs and human health. The paper investigates the possibility of using the artificial intelligence tools in Materials Science. The authors solved the problem of choosing the optimal class of Ti-based powders with a given fractional composition and properties for the manufacture of medical implants by 3D printing. For this purpose, based on the formed data sample, the efficiency of application of a number of existing and improved machine learning algorithms is investigated. Experimental evaluations of their efficiency for different indicators of accuracy and also speed of its work are presented. The application of the optimal machine learning method to solve the problem is determined and substantiated.
CrowdAR Table An AR system for Real-time Interactive Crowd Simulation

Spatial augmented reality, where virtual information is projected into a user’s real environment, provides tremendous opportunities for immersive analytics. In this demonstration, we focus on real-time interactive crowd simulation, that is, the illustration of how crowds move under certain circumstances. Our augmented reality system, called CrowdAR, allows users to study a crowd’s motion behavior by projecting the output of our simulation software onto an augmented reality table and objects on this table. Our prototype system is currently being revised and extended to serve as a museum exhibit. Using real-time interaction, it can teach scientific principles about simulations and illustrate how these, in combination with augmented reality, can be used for crowd behavior analysis.
Enhancing of the operational decisions in Electric Power Systems under blackouts

The operation of electric power systems is a complex task that includes the control and supervision of a great number of elements. A failure in any of them could generate system disturbances and, if they get worse, blackouts can be produced. In this regard, this works presents a model that helps with the decisions in the field of efficiency of these systems. The model analyzes the line status considering the combination of factors that are not frequently addressed in the literature. It allows enhancing the operation decisions by proposing alternative schedules that reduce the possibility of the systems to suffer failures. To prove the effectiveness of the model, a real case is studied: the great blackout of the Argentine System in 2019. Results show alternative solutions, which would have been useful to operate the system during this event, by reducing the power flows in risk lines with only 0.05% of the cost increase.