Miracle-dz/UniFAR-FTUs huggingface dataset:
Feelgood Management in German SMEs and its Impacts on Employees’ Health, Satisfaction and Performance

Feelgood Management is an emerging concept first applied in the German start-up scene in 2012. The approach is gaining popularity, even though the measurement is difficult and academic research is scarce. Accordingly, this study aims to close this research gap by answering the research question about the impact of Feelgood Management in German SMEs, especially on the employees’ heath, satisfaction and performance willingness. Our findings show that Feelgood Management is just emerging and faces several challenges, related to the ambiguous term that implies ridicule, the lack of standardization that is allowing various interpretations and opposition towards novelty. Despite being limited, due to the risk of bias and subjectivity that is natural for qualitative data collection along with the uni-dimensional perspective of solely Feelgood Managers, this study produces a valuable model of the influences on Feelgood Management and its impact on employee health, satisfaction and performance willingness.
Plant height and biomass estimations on wheat using high-resolution aerial imagery in an experimental field in Sonora , Mexico

At present, there are already many global efforts to improve food security, including those that seek to increase crop productivity of small farmers through providing better crop varieties. Wheat is an important source of food intake for many communities, especially in developing countries. Thus, the work of physiological improvement of wheat crop can contribute to accelerate genetic gains using new technologies. This study contributes to explore high throughput phenotyping methodologies, with estimations of plant height and biomass above ground in wheat were obtained using high resolution aerial images from unmanned aerial vehicles in an experimental field in Sonora, Mexico. Two different image resolutions (0.5 cm and two cm), and two different methods of georeferencing the digital threedimensional model were evaluated (using ground control points and geotagging of images during imageacquisition with differential correction on the UAV navigation system). The study confirms promising results in the estimation of plant height (correlations between 0.52 to 0.87) and above-ground biomass (correlations from -0.01 in booting to 0.40 in anthesis stage). The direct georeferencing method using the differential correction has been identified to be the better option, especially, because it is saving time and costs in the data collection process, while providing enough accuracy. The overall image resolution was two cm, but not with great and consistent differences.
Structural and functional microbial diversity of sandy soil under cropland and grassland

Background Land use change significantly alters soil organic carbon content and the microbial community. Therefore, in the present study, the effect of changing cropland to grassland on structural and functional soil microbial diversity was evaluated. The specific aims were (i) to identify the most prominent members of the fungal communities and their relevant ecological guild groups; (ii) to assess changes in the diversity of ammonia-oxidizing archaea; (iii) to determine the relationships between microbial diversity and selected physical and chemical properties. Methods We investigated microbial diversity and activity indicators, bulk density and the water-holding capacity of sandy soil under both cropland and 25-year-old grassland (formerly cropland) in Trzebieszów, in the Podlasie Region, Poland. Microbial diversity was assessed by: the relative abundance of ammonia-oxidizing archaea, fungal community composition and functional diversity. Microbial activity was assessed by soil enzyme (dehydrogenase, β-glucosidase) and respiration tests. Results It was shown that compared to cropland, grassland has a higher soil organic carbon content, microbial biomass, basal respiration, rate of enzyme activity, richness and diversity of the microbial community, water holding capacity and the structure of the fungal and ammonia-oxidizing archaea communities was also altered. The implications of these results for soil quality and soil health are also discussed. The results suggest that grassland can have a significant phytosanitary capacity with regard to ecosystem services, due to the prominent presence of beneficial and antagonistic microbes. Moreover, the results also suggest that grassland use may improve the status of soil organic carbon and nitrogen dynamics, thereby increasing the relative abundance of fungi and ammonia-oxidizing archaea.
Trajectory (Motion) Estimation Of Autonomously Guided Vehicle Using Visual Odometry

: Visual odometry is a technique to determine coordinates of a vehicle or any object by using surrounding visual information. The approach is revolutionary for small autonomously guided vehicles. Because in indoors performance is very poor. And IMUs at small scale are very sensitive to noise. And IMUs can also give unexpected results which may lead to erroneous motion estimation. Technique utilizes some state of the art techniques like feature detection, tracking and some mathematics of camera theory.
Clustering and Prediction of Adjective-Noun Pairs for Visual Affective Computing

One of the main problems in visual Affective Computing is overcoming the affective gap between low-level visual features and the emotional content of the image. One rising method to capture visual affection is through the use of Adjective-Noun Pairs (ANP), a mid-level affect representation. This thesis addresses two challenges related to ANPs: representing ANPs in a structured ontology and improving ANP detectability. The ﬁrst part develops two techniques to exploit relations between adjectives and nouns for automatic ANP clustering. The second part introduces and analyzes a novel deep neural network for ANP prediction. Based on the hypothesis of a different contribution of the adjective and the noun depending of the ANP, the novel network fuses the feature representations of adjectives and nouns from two independently trained convolutional neural networks
SPE-LC-PDA method development and application for the analysis of selected pharmaceuticals in river and wastewater samples from South Africa

The occurrence of pharmaceutical compounds in the environment has been a growing concern. However, there is little information on the concentration levels of pharmaceuticals in African countries. This indicates the need and the importance to develop sensitive methods that can detect these compounds at trace levels (ng/L–μg/L). This work reports on the development of solid phase extraction followed by liquid chromatography–photo diode array (SPE-LC-PDA) analysis. The method was then applied for the analysis of pharmaceutical compounds (ibuprofen, fenoprofen, naproxen, carbamazepine and diclofenac) in river and wastewater samples. The SPE recoveries obtained at 10 000 ng/L spike concentration ranged from 89–120% in distilled water, 79–110% in river water and 78-94% in wastewater. The LODs and LOQs ranged from 10.9–20.4 ng/L and 36.2–60.7 ng/L, respectively. Concentrations of the pharmaceuticals obtained in river water samples ranged from 60 to 32 900 ng/L; in wastewater they ranged from 70 to 66 900 ng/L. Higher concentrations of pharmaceuticals were detected in winter compared to the spring season. Keywords: solid phase extraction pharmaceuticals liquid chromatography photo diode array wastewater
Enhancement of Single Document Text Summarization using Reinforcement Learning with Non-Deterministic Rewards

: A text summarization system generates short and brief summaries of original document for given user queries. The machine generated summaries uses information retrieval techniques for searching relevant answers from large corpus. This research article proposes a novel framework for generating machine generated summaries using reinforcement learning techniques with Non-deterministic reward function. Experiments have exemplified with ROUGE evaluation metrics with DUC 2001, 20newsgroup data. Evaluation results of proposed system with hypothesis of automatic summarization from given datasets prove that statistically significant improvement for answering complex questions with f-actual vs. critical values.
A Novel Strategy for Multi-Objective Tracking Framework based on Semi-online Mechanism

—Multi-similar appearance and occlusion problem are still the main difficult issues in the field of multi-target tracking. Many practical multi-object tracking (MOT) algorithms based on detectors widely used in industry only rely on the detector's Intersection over Union (IOU) to solve most tracking problems. In this paper, we propose a MOT strategy based on a semi-online mechanism, which includes IOU matching strategy, trajectory exploration strategy and backtracking mechanism. Meanwhile, the integrated motion model and the enhanced appearance model are also presented. According to the tracking temporal window, our strategy will find multiple pairs of detection frames by matching the largest IOU first, and then it leverages Kalman filter to establish a motion model for exploring various tracking trajectory. At last, it calculates the reliability of different tracking trajectories and executes regret strategy we proposed to generate optimized tracking trajectory. Our algorithm focuses on transforming the tracking issues into how to generate robust fragments of tracking trajectory,and how to fix the back-tracking errors within the temporal window according to chronological order of tracking context. Our method is naturally suitable for solving the difficult issues such as disappearance, detection noise, and target occlusion. We evaluate our algorithms on the datasets of MOT2015 and MOT2017. Compared with baseline algorithm, our method reduces 16 IDS on each video sequence on average, and the multi-target tracking accuracy has been improved by 4.01%. Our algorithm model is very versatile and efficient for the algorithms of short trajectory stitching.
Data Mining based Framework for Effective Intrusion Detection using Hybrid Feature Selection Approach

— Building strong IDS is essential in today’s network trafﬁc environment, feature reduction is one approach in constructing the effective IDS system by selecting the most relevant features in detecting most known and unknown attacks. In this work, proposing the hybrid feature selection method by combining Mutual Information and Linear Correlation Coefﬁcient techniques (MI-LCC) in producing the most efﬁcient and optimized feature subset. Support Vector Machine (SVM) classiﬁcation technique being used in accurately classifying the traffic data into normal and malicious records. The proposed framework shall be evaluated with the standard benchmarked datasets including KDD-Cup-99, NSL-KDD, and UNSW-NB15 datasets. The test results, comparison analysis and reference graphs shows that the proposed feature selection model produces optimized and most important features set for classiﬁer to achieve stated accuracy and less false positive rate compared with other similar techniques.
A Flow-Based Technique to Detect Network Intrusions Using Support Vector Regression (SVR) over Some Distinguished Graph Features

A bstract: Today unauthorized access to sensitive information and cybercrimes is rising because of increasing access to the Internet. Improvement in software and hardware technologies have made it possible to detect some attacks and anomalies effectively. In recent years, many researchers have considered flow-based approaches through machine learning algorithms and techniques to reveal anomalies. But, they have some serious defects. By way of illustration, they require a tremendous amount of data across a network to train and model network’s behaviors. This problem has been caused these methods to suffer from desirable performance in the learning phase. In this paper, a technique to disclose intrusions by Support Vector Regression (SVR) is suggested and assessed over a standard dataset. The main intension of this technique is pruning the remarkable portion of the dataset through mathematics concepts. Firstly, the input dataset is modeled as a Directed Graph (DG), then some well-known features are extracted in which these ones represent the nature of the dataset. Afterward, they are utilized to feed our model in the learning phase. The results indicate the satisfactory performance of the proposed technique in the learning phase and accuracy over the other ones.
A NOVEL CRYPTOGRAPHIC APPROACH BASED ON FEEDBACK MODE OF ELGAMAL SYSTEM

In this work we combine concept of running key cipher with ELGamal theory. so in this process we use two type of cryptography public key cryptography and private key cryptography by using single public private key pair. To do so we use Running Key Cipher followed by ElGamal in feedback mode. We construct a cryptographic scheme by using only single step of ElGamal providing security to the entire message blocks involved. We then analyze the performance and security of our proposal with respect to existing system
Design of refractive laser beam shaper to produce flat-top profile irradiance in order to use in pumping system of thin disk lasers

One of the most prominent parameters in thin disk lasers’ performance is their pump profile making temperature gradient in disk. This phenomenon is able to distort laser’s wavefront, especially in high power operation, by two physical properties of disk including temperature-dependent refractive index and thermal expansion. It is clear that as the pump profile uniformity (super-Gaussian coefficient) increases, the amount of temperature gradient and wavefront distortion will proportionally reduce. In this paper, a refractive laser beam shaper has been simulated and designed in order to generate a circular flat-top profile on disk surface with 12.48 mm diameter and super-Gaussian coefficient of 16.4. The system has been, also, experimented practically that its results were similar to numerical ones, the spot diameter and super-Gaussian coefficient were 12.42 mm and 18.6, respectively.
Interannual modulation of the Pacific Decadal Oscillation (PDO) on the low-latitude western North Pacific

To investigate the interannual variability in the northwestern Pacific, an empirical mode decomposition (EMD) was applied to 17-year Absolute Dynamic Topography (ADT) data west of Luzon Island, the Philippines. The mean sea surface height in this area is an appropriate index for the Kuroshio intrusion into the South China Sea (SCS). Significant interannual fluctuations were extracted by the EMD. The interannual variability was strongly correlated with the Pacific Decadal Oscillation (PDO) index, but not the El Niño–Southern Oscillation (ENSO). This indicated the potential impact of the PDO on the circulation in the area. In the warm phase of the PDO (positive index), a southerly anomalous wind off the Philippines causes a northward shift of the North Equatorial Current Bifurcation Latitude (NECBL). This leads to a weakened Kuroshio off Luzon, favoring Kuroshio intrusion into the SCS. The northward migration of the NECBL also results in a weakened Kuroshio off southeast Taiwan and a larger Kuroshio transport off northeast Taiwan. The abundant westward propagating eddies impinging on the Kuroshio in the Subtropical Countercurrent region increases this transport. Although the ENSO has little effect on monsoonal winds during the warm PDO phase, it has a strong impact on the monsoon and meridional migration of the NECBL during the cold phase of the PDO. Therefore, NECBL variations only show a close correspondence with the ENSO during the cold PDO phase. Because the influence of the ENSO is not stationary, the impact of the PDO should be taken into account when examining interannual variability in the low-latitude western North Pacific. 2012 Elsevier Ltd. All rights reserved.
Diagnosis and treatment of collinearity problems and variable selection in least-squares models

Collinearity problems may occur in regression models when independent variables are correlated. Collinearity results in regression coefficients with roundoff errors, unstable estimates, incorrect signs, and inflated variances. Condition indices and variance decomposition proportions quantitatively measure the severity of the problem and should be calculated for all multiple regressions. Principal component and ridge regressions treat collinearity. Procedures for selecting the optimum regressor variables and determining their interrelationships help alleviate collinearity and other problems associated with regression.
Title : Feasible Performance Evaluations of Digitally-Controlled Auxiliary Resonant Commutation Snubber-Assisted Three Phase Vo

The AC/DC converter is one of the popular power electronic converters in industrial applications such as in the railway, power supply systems and electric vehicle. In this paper, a three-phase controllable rectifier is considered and its linear model is extracted. Because of MPC controllers benefits, the continuous control set model predictive controller (CCs-MPC) is designed for controlling this rectifier output DC voltage. By considering rectifier dynamic response, the suitable criteria to choice the model predictive controller parameters such as sampling time, prediction horizon and control horizon is proposed. In experimental implantation the computing burden of microcontroller is limit therefore the reaching to optimal and minimum complexity in algorithms implantation is vital problem. In other words by using these proposed criteria for selection of sample time, prediction and control horizon the tradeoff between computational burden, system performance and dynamic stability is made. When using designed MPC controller, the rectifier and grid performance such as total harmonic distribution (THD), power factor (PF) and output voltage ripple have acceptable value. This controller can eliminated the effect of heavy load change on rectifier performance which is very common problem in industrial system. Also, this controller stability guaranteed is checked by using the dual-mode method. The simulation results and controllers performance are validated in MATLAB software
Paleomagnetism of Diabase Dykes, Pegmatitic Granites and TGG Gneisses in the Olkiluoto Area

Paleomagnetic studies in the Olkiluoto area were carried out on two diabase dykes, on four sites of pegmatitic granites and on three sites of TGG gneisses. Remanent magnetizations in the diabase dykes are strong, and two remanence components were isolated. The other component is regarded as primary, formed during cooling of the dykes. Based on comparison to previously known Fennoscandian paleomagnetic data, the pole position of the primary component shows that the dykes are ca. 1560 Ma in age. The pole position of the poorly defined secondary component of the diabase dyke gives an age of ca. 250 Ma. Pegmatitic granites are weakly magnetized, but one pegmatitic granite site gave a rather consistent remanence direction which points to a remagnetization at ca. 1570 Ma, probably due to emplacement of the nearby rapakivi granites. The TGG gneisses do not carry any stable remanent magnetization.
Off-Policy Reinforcement Learning for Autonomous Driving

Modern autonomous driving systems continue to face the challenges of handling complex and variable multi-agent real-world scenarios. Some subsystems, such as perception, use deep learning-based approaches to leverage large amounts of data to generalize to novel scenes. Other subsystems, such as planning and control, still follow the classic cost-based trajectory optimization approaches, and require high efforts to handle the long tail of rare events. Deep Reinforcement Learning (RL) has shown encouraging evidence in learning complex decision-making tasks, spanning from strategic games to challenging robotics tasks. Further, the dense reward structure and modest time horizons make autonomous driving a favorable prospect for applying RL. As there are practical challenges in running RL online on vehicles and most self-driving companies have millions of miles of collected data, it motivates the use of off-policy RL algorithms to learn policies that can eventually work in the real world. We explore the use of an off-policy RL algorithm, Deep Q-Learning, to learn goal-directed navigation in a simulated urban driving environment. Since Deep Q-Learning methods are susceptible to instability and sub-optimal convergence, we investigate different strategies to sample experiences from the replay buffer to mitigate these issues. We also explore combining expert agent’s demonstration data with the RL agent’s experiences to speed-up the learning process. We demonstrate promising results on the CoRL2017 and NoCrash benchmarks on CARLA.
Xen 3.0 and the Art of Virtualization

The Xen Virtual Machine Monitor will soon be undergoing its third major release, and is maturing into a stable, secure, and full-featured virtualization solution for Linux and other operating systems. Xen has attracted considerable development interest over the past year, and consequently the 3.0 release includes many exciting new features. This paper provides an overview of the major new features, including VM relocation, device driver isolation, support for unmodiﬁed operating systems, and new hardware support for both x86/64 and IA-64 processors.
Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation

Just as SARS-CoV-2, a new form of coronavirus continues to infect a growing number of people around the world, harmful misinformation about the outbreak also continues to spread. With the goal of combating misinformation, we designed and built Jennifer–a chatbot maintained by a global group of volunteers. With Jennifer, we hope to learn whether public information from reputable sources could be more effectively organized and shared in the wake of a crisis as well as to understand issues that the public were most immediately curious about. In this paper, we introduce Jennifer and describe the design of this proof-of-principle system. We also present lessons learned and discuss open challenges. Finally, to facilitate future research, we release COVID-19 Question Bank, a dataset of 3,924 COVID-19-related questions in 944 groups, gathered from our users and volunteers.
Learning Label-Relational Output Structure for Adaptive Sequence Labeling

Sequence labeling is a fundamental task of natural language understanding. Recent neural models for sequence labeling task achieve significant success with the availability of sufficient training data. However, in practical scenarios, entity types to be annotated even in the same domain are continuously evolving. To transfer knowledge from the source model pre-trained on previously annotated data, we propose an approach which learns label-relational output structure to explicitly capturing label correlations in the latent space. Additionally, we construct the target-to-source interaction between the source model MS and the target model MT and apply a gate mechanism to control how much information in MS and MT should be passed down. Experiments show that our method consistently outperforms the state-of-the-art methods with a statistically significant margin and effectively facilitates to recognize rare new entities in the target data especially.