Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter

In recent times, software developers widely use instant messaging and collaboration platforms, as these plat-forms aid them in exploring new technologies, raising different development-related issues, and seeking solutions from their peers virtually. Gitter is one such platform that has a heavy userbase. It generates a tremendous volume of data, analysis of which is helpful to gain insights about trends in open-source software development and the developers' inclination toward various technologies. Analyzing these trends helps these platforms better cater to the needs of the developers, in turn increasing the usage of these platforms and promoting collaborations between more developers. The classification techniques can be deployed for this purpose. The selection of an apt word embedding for a given dataset of text messages plays a vital role in determining the performance of classification techniques. In the present work, the comparative analysis of nine-word embeddings in combination with seventeen classification techniques with onevsone and onevs-rest has been performed on the GitterCom dataset for categorizing text messages into one of the pre-determined classes based on their purpose. Further, two feature selection methods have been applied. The SMOTE technique has been used for handling data imbalance. It resulted in a total of 1836 classification pipelines for analysis. The objective is to analyze their performances to recommend efficient pipelines for the classification task at hand. The experimental results show that word2vect, GLOVE with 300 vector size, and GLOVE with 100 vector size are three top-performing word embeddings having performance values taken across different classification techniques. The models trained using ANOVA features performed similarly to those models trained using all features. Finally, using the SMOTE technique helps models to get a better prpediction ability.
The Effect of Financial Literacy on the Investment Decisions of Investment Applications Users

This research was conducted with the aim of knowing the effect of financial behavior, financial attitude, and financial literacy on the decisions of investment application (with robo advisor technology) users. The total population in this study was 270.661 people, and a total of 471 respondents filled out the questionnaires online. The validity and reliability tests were also carried out to test whether a research instrument can be used as a variable measuring tool in the study. The results of this study indicate that financial behavior has a positive effect on the investment decisions of the investment application users. Financial attitudes have a significant positive effect, and financial literacy also have a significant effect on investment decision making. The findings show that long-term intentional efforts are essential for increasing financial literacy. Therefore, it is important to evaluate people's decision-making processes to enable them to investigate whether they display positive behavior and attitude financially, which can result in increased resilience in times of crisis, in encouraging greater financial well-being and bigger financial satisfaction.
Mode de production et systèmes de culture et d'élevage

[eng] Mode of production and crop or livestock systems - The author intends to show how the theory of Marx can be used to explain the development of farm production systems. The unequal development which characterizes the economic and social capitalistic system results precisely in an unequal integration of the farms into a market economy and consequently in a unequal access to modern production techniques. In order to remain competitive, or at least to delay their elimination, the small farms underpay their labour which is possible in a family-farm system with a subsistence economy. The big farms, on the contrary, which are solidly integrated in the market economy dispose of the capital necessary to conciliate the use of high productivity techniques and the employment of hired labour. In countries where an agrarian reform has been carried out, for instance in the DRG or in Hungary, the main preoccupation has been the uniform development of the farms and has resulted in a homogenization of the production systems, each farm practising both market and non-market activities. [fre] L'auteur se propose de montrer comment la theorie de Marx peut aider a rendre compte de l'evolution des systemes de production des exploitations agricoles. Le developpement inegal, qui caracterise un systeme economique et social capitaliste, se traduit notamment par une inegale integration des exploitations agricoles dans l'economie marchande et correlativement par un inegal acces aux techniques modernes de production. Pour rester competitives, ou tout au moins retarder leur elimination, les petites exploitations sont astreintes a sous-payer leur force de travail, ce que leur permet precisement le caractere familial de leur main-d'œuvre et les pratiques d'autosubsistance. Par contre, les grandes exploitations, fortement integrees dans l'econome marchande, ont le capital monetaire necessaire pour concilier l'emploi de techniques a forte productivite et la remuneration d'une main-d'œuvre salariee. Dans les pays qui ont fait une revolution agraire, par exemple la R.D. A. ou la Hongrie, le souci de promouvoir un developpement egalitaire s'est traduit notamment par une tendance a l'homogeneisation des systemes de production, chaque exploitation ayant sa part de productions commercialisables et non commercialisables.
Over-the-Air Federated Transfer Learning Over UAV Swarm for Automatic Modulation Recognition in V2X Radio Monitoring

Theincreasing number of smart vehicles is leading to an increasing scarcity of spectrum resources for the internet of vehicles (IoV), which has given rise to an urgent requirement for automatic modulation classification (AMC) in cognitive radio (CR) systems. Meanwhile, for the flexibility of unmanned aerial vehicles (UAVs), the AMC implemented based on UAVs is considered an effective method to achieve reliable communication between intelligent vehicles. However, for distributed UAV task implementation, real-time radio data needs to be transmitted between UAVs and a cloud server. This process requires maintaining a high-capacity, secure channel environment, which is difficult to accomplish. In this article, we propose a federated transfer learning framework to implement AMC in a distributed scenario, which avoids radio data transmission in each UAV. To reduce data dependence, the pre-trained deep learning (DL)-based model is sent to each UAV node and performs transfer learning, which brings more focused learning of the channel environment in which various UAVs are located. The simulation results show that federated transfer learning-based AMC offers better recognition accuracy than centralized approach. Compared to the centralized training methods, the federated transfer learning algorithm achieves an improvement of 1.04% to 12.05% in classification accuracy for each node with less training data. Besides, the effect of different fine-tuning layers on the accuracy is investigated, showing that fine-tuning three layers could achieve optimal accuracy. Additionally, different numbers of UAVs are employed to verify the impact on the results. The experimental results show that the number of UAVs can improve the results but to a limited extent. Furthermore, we evaluate the proposed method by various measurements, such as accuracy, precision, and F1-score. Accordingly, compared with the baseline methods, the proposed scheme achieves an improvement of 1% to 14% over them.
Radio interferometer measurements of plasmasphere density structures during geomagnetic storms

The Los Alamos plasmaspheric drift radio interferometer is a ground-based array that regularly measures periodic disturbances in the plasmasphere. These plasmaspheric density structures have been shown to depend on geomagnetic activity, as indicated by Kp. However, a direct storm time analysis of their behavior has not been done. This paper studies the amplitude, drift velocity, and location of these structures before, during, and after the onset of major geomagnetic storms. Distinct large-amplitude, storm time signatures are found during the first night after onset, continuing through the third night; there were significantly more storm time signatures during nighttime than daytime. The L shells on which the disturbances existed were found to decrease after storm onset, indicating a possible shrinking of the plasmasphere.
A quick route planning algorithm in vehicle navigation system

A quick route planning algorithm is proposed in the paper to improve searching efficiency and shorten calculating time. In order to reduce the data of computing and storage, the road network is divided into several small road networks called division road networks at first. The division road network is connected by boundary nodes and boundary edges. The start point and the end point are belonging to different division road networks. Then, the shortest path among the start point, the end point and boundary nodes is searched, the shortest path from the beginning point to the end point can be found by combining these shortest paths. By dividing the road network, searching space is reduced and searching time is shortened. Moreover, due to the shortest paths between boundary nodes are calculated and saved in computer in advance, so the computing time is shortened a lot. In the end of the paper, a practical application example of the algorithm in realtime vehicle navigation systems is given. The experimental results show that it's a good and efficiency way of calculating the shortest path between two arbitrary points within the road network.
Deep Cross-Modal Retrieval Between Spatial Image and Acoustic Speech

Cross-modal Retrieval (CMR) is formulated for the scenarios where the queries and retrieval results are of different modalities. Existing Cross-modal Retrieval (CMR) studies mainly focus on the common contextualized information between text transcripts and images, and the synchronized event information in audio-visual recordings. Unlike all previous works, in this article, we investigate the geometric correspondence between images and speech recordings captured in the same space and formulate a novel CMR task, called Spatial Image-Acoustic Retrieval (SIAR). To this end, we first design a novel speech encoder that consists of convolution neural networks and transformer layers, to learn space-aware speech representations. Then, to eliminate the cross-modal inherent discrepancy, we propose the Contrastive Speech Image Retrieval (CSIR) method which uses supervised contrastive learning to attract the same-space cross-modal features while repelling the ones from different spaces. Finally, image and speech features are directly compared and we predict the SIAR result with the maximum similarity. Extensive experiments demonstrate that our proposed speech encoder can recognize space from human speeches with superior performance over the other prevailing networks. It also sets our penultimate goal of speech-to-speech retrieval. Furthermore, our CSIR proposal can successfully perform bi-directional SIAR between spatial images and reverberant speeches with promising results. Code and data will be available.
Scanning Electron Microscopy–Energy Dispersive Scan Analyses and Rheological Investigations of South-Brazilian Soils

Scanning electron microscopy, including energy dispersive scan analyses and amplitude sweep tests with controlled shear deformation, were conducted on four kaolinitic, Fe-oxide-rich Brazilian Hapludox soils and one montmorillonitic Calciudert soil to elucidate results obtained from a parallel-plate rheometer. In the Brazilian Hapludox soils, the occurrence of pseudosand (an aggregation of fine particles with a grain size of sand), due to Fe (hydr)oxide cementation, had a significant effect on aggregation and microstructural stability. We could observe the presence of hematite, goethite, and, in rare cases, halloysite, as the major stabilizing minerals in such soils. The influences of soil organic matter (SOM), Fe oxides, and clay minerals on micromechanical shear behavior under oscillation were tested under saturated and predrained (at -60 hPa) conditions. Collected data included G (shear modulus), G' (storage modulus), G'' (loss modulus), linear viscoelastic deformation range, deformation limit (gamma L), and yield stress (tau y). From the data, the dissipation of elasticity was derived, and the values ranged from 200 to 1000 Pa in all untreated samples, both Hapludox and Calciudert soils, and to 50 to 500 Pa in SOM-leached Hapludox samples. Minimum tau y values of 10 to 50 Pa occurred after a sodium dithionite treatment (Fe(d) leached) of the four Hapludox samples. These findings show that Fe (hydr)oxides have a more stabilizing effect on the microstructural stability than SOM.
Microbial communities in deep Canadian shield groundwaters—an in situ biofilm experiment

Microbial biofilm communities were cultivated on stainless steel and polypropylene surfaces within brine‐filled exploration boreholes in crystalline rocks of the Canadian Shield at Kidd Creek Mine (Timmins, Ontario) and Copper Cliff South Mine (Sudbury, Ontario) at depths of 1402 m and 1219 m, respectively. The calcium‐sodium‐chloride brines were acidic (pH 3.5–4.8) and had temperatures of 23.4°C to 18.6°C. Direct microscopic counts using DAPI (4’,6‐diamidino‐2‐phenylindole) epifluorescent staining revealed coccoid‐shaped, vibroid, rod‐shaped, and filamentous bacteria in Copper Cliff South biofilms. The same morphological types were observed at Kidd Creek with the addition of a group of large (2–3 μm in length) vibroid to rod‐shaped bacteria. Average total counts were 9 ±7 × 106 bacteria/cm2 for Copper Cliff South biofilms and 5 ± 1 × 106 bacteria/cm2 for Kidd Creek biofilms with a mean biomass value of 131 ± 35 μg carbohydrate/cm2. Transmission electron microscopy revealed slightly vibroid, rod‐shaped, g.
Analysis of matching algorithm for infrared images based on anisotropic diffusion equation

We present an algorithm for infrared target recognition based on edge features, aiming to improve the performance of recognition within low contrast and low SNR infrared images. The main process including: using nonlinear anisotropic diffusion equation to smooth noise as preprocessing after enhancing the image contrast through equalizing the image gray-level histogram, so as to filter out noise as well as maintain edge and shape features; then considering the greater differences between the target template and the fragmentary edges extracted directly from infrared images, Otsu threshold segmentation algorithm is selected to extracted the region feature before we use Canny operator to extract target edge features; finally, we match the target edge template with infrared images to recognize the target, using Hausdorff distance as a matching measure. Through experiment on several infrared image sequences, the results show that comparing with other preprocessing algorithms, our method achieve better performance for target recognition.
Towards Designing Adaptive and Personalized Work Systems in Manufacturing

The manufacturing sector in Europe still holds significant importance. Within the European Union's (EU) non-financial business economy, manufacturing industry accounts for around 29.7 per cent of the gross value added and 23.1 per cent of the employment (Eurostat 2021). For Austria, the number of employees in industry is even expected to increase over the upcoming five years (Patsch et al. 2021). Thus, human labour still plays a significant part in manufacturing.
Sound simulation of granular materials

In recent years, methods for simulating sounds of solid objects, fluids, fire and cloth have been developed. These methods extend existing methods for simulation of the visual behaviour of the respective phenomena and add physically based sounds that correspond with the visual information. The goal of this thesis is to investigate the possibility of creating a similar method for sound simulation of dry granular materials. We identified two distinct mechanisms responsible for the sounds of granular materials: the collisions of the particles of the granular material (granular collisions) and the collisions between the particles and a surrounding solid objects (solid objects collisions). We propose a method that uses acceleration noise to simulate the sounds of the granular collisions. The particles are approximated by a simplified spherical shapes. We also simulate the sound of the solid object collisions by using a modal analysis approach. Since most of the approaches to the visual simulation of granular materials do not provide the information about the particle collision, we also present a probabilistic approach to random generation of collision data.
A surface-subsurface study of reservoir-scale fracture heterogeneities in Cretaceous carbonates, North Oman

Abstract Reservoir-scale structural heterogeneity, especially in terms of mechanical layering and natural fracture systems, is often insufficiently constrained by subsurface data alone. In North Oman, a large dataset in Cretaceous carbonates comprises data from multiple subsurface reservoirs and analogue outcrops. This provides an ideal opportunity to integrate outcrop constraints into the subsurface, and to calibrate the resulting models dynamically. For this purpose, a reservoir-scale analogue outcrop fracture template was created for the Jebel Madmar anticline in the Oman Mountains foothills. The outcrop template provides improved conceptual and quantitative constraints on (i) fracture types and dimensions (e.g. of NE-trending fracture corridors), (ii) fracture heterogeneity, both aerially and stratigraphically, (iii) fracture properties (e.g. cementation evolution, variations due to preferential fault/fracture reactivation) and (iv) structural evolution and history of reactivation. Within a regionally consistent structural framework, the outcrop template has greatly assisted in the creation of geologically realistic models for one of the fractured carbonate reservoirs, complementing the subsurface dataset. Initial dynamic calibration indicates successful application of the outcrop template in that the spatial fracture heterogeneity was succesfuUy captured in the reservoir models and provides a history match to production data. The reduced range of possible fracture system geometries in turn has provided better constraints on the effective fracture properties.
Screening of sunflower (Helianthus annuus L.) cultivars/hybrids for heat stress tolerance using growth and physiobiochemical indicators

: High temperatures may cause scorching of the twigs and leaves along with visual symptoms of sunburn, leaf senescence, growth inhibition, and ultimately decreased plant growth and biomass. Sunflower ( Helianthus annuus L.) is one of the important oil seed crops and it potentially fits in agricultural system and oil production sector of Pakistan. Hence, it is important to get the best variety which may able to tolerate high temperatures in hot and humid environments. For this purpose, we have conducted the present study using ten different cultivars of (Hysin-33, T-40, H-OI, Hysin-39, Suncross, Gulshin, FH-825, FH-797, FH-784, AGSUN-5270) of H. annuus grown in the growth chambers in two different temperatures, i.e. control (25 °C) and heat stress (45 °C). A pot experiment was conducted and contains nutritional sand medium in the controlled environment in the growth chamber for 4 weeks. Results from the present study showed that heat stress induced a significant (p < 0.05) decrease in shoot length, root length, shoot fresh weight, root fresh weight, shoot dry weight, root dry weight, chlorophyll a, chlorophyll b, total chlorophyll, and carotenoid content while increasing the production of reactive oxygen species (ROS) by increasing the concentration of malondialdehyde (MDA) and hydrogen peroxide (H 2 O 2 ), which is manifested by increasing content of flavonoid and phenolic. Results also showed that Hysin-39, FH-825, and Hysin-33 showed better growth and development in heat-stressed environment and considered a heat-tolerant cultivar while Gulshin, Suncross, and AGSUN-5270 showed poor growth and development in the same stressed environment and were considered heat-sensitive cultivars. The overall trend of H. annuus cultivars grown under heat stress is as follows: Hysin-39 > FH-825 > Hysin-33 > T-40 > H-OI > FH-784 > FH-797> AGSUN-5270 > Suncross > Gulshin.
Tree volume estimation for small silvopastoral systems: do we really need the height variable to obtain su�ciently accurate results?

11 The objective of this study was to evaluate and compare the performance of single and double-entry 12 volumetric models in estimating tree volume of eucalypt trees in different silvopastoral systems in Coronel 13 Pacheco, Minas Gerais State, Brazil. Diameter at breast height, total height, and diameter outside the bark 14 along the stems of the sample trees were recorded. Four single-entry and five double-entry volume models 15 were fitted to the observed data for six strata consisting of different Eucalyptus genetic material and three 16 silvopastoral systems, and were compared to select the best alternative. Double-entry models, specifically 17 logarithmic Spurr and logarithmic Schumacher & Hall, fitted statistically better then single-entry ones for 18 all but one of the strata, where they were overcome by the Husch’s model. However, although the 19 superiority of the former can have been easily verified by different and complementary statistics, we found 20 that the volume estimates provided by the
Experimental Evidence for the Emergence of Large Particles in the Gravity‐Induced Shear Flow of Smaller Cohesionless Grains

The surfacing process of a single large granular particle in the shear flow composed of smaller dry, cohesionless grains was explored through an inclined‐chute experiment using a video camera. The experiment indicated that the large particle climbed obliquely upward, rolling over small grains. A simple mechanism, but one never observed before, emerged: rotational motion of large particles, caused by shear rate increasing toward the flow surface, is responsible for boosting the particles.