Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Pilot Fatigue in Commercial Air Transportation

Fatigue is a major human factor related cause of aviation accidents and currently subject to increased discussion by aviation authorities and professional pilots. Reducing fatigue and minimizing the risk of fatigue induced errors requires predicting the alertness state of crew members and identifying possible fatigue sources. Bio-mathematical models are a way to estimate fatigue levels of crew members based on work schedules. These models are currently utilized in commercial aviation to various degrees. In this thesis a prediction model for estimating fatigue is developed. It is based on theory of sleep and fatigue and previous research on bio-mathematical models. Fatigue data was gathered from pilots working on short-haul operations and it is used to estimate parameters and validate the model. Work schedules are used as input and an alertness score based on Karolinska Sleepiness Scale is estimated. The aim is to estimate fatigue in the work schedule planning phase, weeks in advance of actual date of operations. The sources for fatigue in the model are time of day, time worked and presence of consecutive early morning shifts. Time not in work is defined as recovery period, which decreases fatigue. Cumulative effects were not identified to have significant effect on fatigue with available data. The results indicate that it is possible to develop a model that estimates fatigue adequately, but the personal differences how people feel and experience fatigue make it difficult to create an applicable average model that fits well for everyone. Estimating parameters for every individual increases the accuracy and makes the model more feasible, but that is not practical for extensive use in flight operations due to data and time requirements. The developed model is, however, usable as a risk management tool in order to identify fatigue hazards, but it cannot be used as a sole basis for decision making due to limited accuracy. The greatest problem is the lack of sleep data as the amount of sleep has major impact on fatigue levels. Models used to estimate fatigue in advance need to either estimate the amount of sleep based on some probabilistic method or omit it from the model completely. In this thesis sleep was not included in the model.
Toward Semantic Scene Understanding for Fine-Grained 3D Modeling of Plants

Agricultural robotics is an active research area due to global population growth and expectations of food and labor shortages. Robots can potentially help with tasks such as pruning, harvesting, phenotyping, and plant modeling. However, agricultural automation is hampered by the difficulty in creating high resolution 3D semantic maps in the field that would allow for safe manipulation and navigation. In this paper, we build toward solutions for this issue and showcase how the use of semantics and environmental priors can help in constructing accurate 3D maps for the target application of sorghum. Specifically, we 1) use sorghum seeds as semantic landmarks to build a visual Simultaneous Localization and Mapping (SLAM) system that enables us to map 78\\% of a sorghum range on average, compared to 38% with ORB-SLAM2; and 2) use seeds as semantic features to improve 3D reconstruction of a full sorghum panicle from images taken by a robotic in-hand camera.
A Survey on CommTrust: Computing Multi-Dimensional Trust by Mining E-Commerce Feedback Comments

: Reputation-based trust models are widely used in e-commerce applications, and feedback ratings are aggregated to compute sellers’ reputation trust scores. The “all good reputation” problem, however, is prevalent in current reputation systems— reputation scores are universally high for sellers and it is difﬁcult for potential buyers to select trustworthy sellers. In this paper, based on the observation that buyers often express opinions openly in free text feedback comments, a survey on CommTrust for trust evaluation by mining feedback comments is done. Main contributions include: 1) a multidimensional trust model for computing reputation scores from user feedback comments; and 2) an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining, and topic modeling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the “all good reputation” issue and rank sellers effectively.
Impact of job satisfaction on turnover intention among academic personnel of private higher education sector in Iraq: A case of Tishk International University

The aim of this research is to find, test, and explore the relationship between employee's job satisfaction and turnover intention among the academic personnel of Tishk International University in Erbil, to find whether there is a relationship between job satisfaction and turnover intention and whether job satisfaction predicts turnover intention or not. The study consisted of 98 samples chosen randomly from Tishk International University academic employees, and correlation and regression analysis were used to interpret the collected data. As a result, we have found a robust negative relationship between job satisfaction and turnover intention, and job satisfaction can significantly predict turnover intention. © 2022 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Myosin Light Chain Phosphatase Endothelial Permeability via Rho Kinase and Toxin Increases Pasteurella multocida

Pasteurella multocida toxin (PMT) has been shown to induce actin reorganization through activation of the GTPase Rho. Here we investigated the involvement of the Rho target proteins Rho kinase and myosin light chain (MLC) phosphatase in the PMT-induced increase in endothelial permeability and the underlying actin reorganization of endothelial cells. Stimulation of endothelial layers with PMT enhanced transendothelial permeability > 10-fold, and this was abolished by pretreatment with the speciﬁc Rho inactivator C3 transferase from Clostridium botulinum . The PMT-induced increase in endothelial permeability was associated with 1) inactivation of MLC phosphatase, 2) an increase in MLC phosphorylation, and 3) endothelial cell retraction and actin stress ﬁber formation. PMT-stimulated actin reorganization could be prevented by 1) pretreatment of cells with C3 transferase, 2) microinjection of the Rho binding domain and the pleckstrin homology domain of Rho kinase, and 3) microinjection of constitutively active MLC phosphatase. Together, these results suggest that PMT activates Rho/Rho kinase, which inactivates MLC phosphatase. The resulting increase in MLC phosphorylation causes endothelial cell retraction and a rise in endothelial permeability. The Journal of Immunology, 1998, 161: 5640–5646.
Fake Face Image Detection Using Deep Learning-Based Local and Global Matching

The widespread adoption of face recognition systems in practice has provoked multiple attempts to fail these systems in order to impersonate another person. The range of such fake attacks is wide, and methods which can be used to compensate for one type of attacks are not adapted against other attacks. In this study, we propose a method for detecting fake face images based on local and global matching provided by deep neural networks. Also we do not discard the background analysis as a pre-processing stage. The idea is to assess the depth of the face in a still image as one of the main features of liveliness, which is not an easy task. The proposed method is directed against presentation attacks and attacks of adversarial perturbations. The experiments were conducted with and without deep neural networks. The use of deep learning increased the true accept rate and significantly reduced the error values.
Declarative Description of Knowledge Graphs Construction Automation: Status & Challenges

Nowadays, Knowledge Graphs (KG) are among the most powerful mechanisms to represent knowledge and integrate data from multiple domains. However, most of the available data sources are still described in heterogeneous data structures, schemes, and formats. The conversion of these sources into the desirable KG requires manual and time-consuming tasks, such as programming translation scripts, defining declarative mapping rules, etc. In this vision paper, we analyze the trends regarding the automation of KG construction but also the use of mapping languages for the same process, and align the two by analyzing their tasks and a few exemplary tools. Our aim is not to have a complete study but to investigate if there is potential in this direction and, if so, to discuss what challenges we need to address to guarantee the maintainability, explainability, and reproducibility of the KG construction.
Effectiveness of Square Stepping Exercise versus Balance Training Exercises on Fear of Fall and Balance in Parkinsonism Patients

To study the effect of square stepping exercise and balance exercise to improve balance impairment and reducing the fear of falling on Parkinsonism patient. This is a pretest and post test experimental design comparative in nature. Subject aged between 60 – 70 years having Parkinson’s disease were included for the study. Group A (15 subjects) participated in square stepping exercise for 5times in a week for 4 weeks and Group B (15 subjects) participated in balance exercise for 5 times in a week for 4 weeks. The outcome was measured by using Berg balance scale, Time up and go (TUG) test and Fall efficacy scale. Statistical analysis done by using pair ‘t’ test and unpair ‘t’ test showed that there is significant improvement in subject who underwent the square stepping exercise program compared to balance exercise on fear of fall and balance in parkinsonism patients. It is concluded that square stepping exercise group exhibited statistically significant improvement in Berg Balance Scale, Time up and Go test score and fall efficacy test score compared to the balance training exercise group.
Technological quality requirements for stereotactic radiotherapy Expert review group consensus from the DGMPWorking Group for Physics and Technology in Stereotactic Radiotherapy

This review details and discusses the technological quality requirements to ensure the desired quality for stereotactic radiotherapy using photon external beam radiotherapy as defined by the DEGRO Working Group Radiosurgery and Stereotactic Radiotherapy and the DGMP Working Group for Physics and Technology in Stereotactic Radiotherapy. The covered aspects of this review are 1) imaging for target volume definition, 2) patient positioning and target volume localization, 3) motion management, 4) collimation of the irradiation and beam directions, 5) dose calculation, 6) treatment unit accuracy, and 7) dedicated quality assurance measures. For each part, an expert review for current state-of-the-art techniques and their particular technological quality requirement to reach the necessary accuracy for stereotactic radiotherapy divided into intracranial stereotactic radiosurgery in one single fraction (SRS), intracranial fractionated stereotactic radiotherapy (FSRT), and extracranial stereotactic body radiotherapy (SBRT) is presented. All recommendations and suggestions for all mentioned aspects of stereotactic radiotherapy are formulated and related uncertainties and potential sources of error discussed. Additionally, further research and development needs in terms of insufficient data and unsolved problems for stereotactic radiotherapy are identified, which will serve as The authors D. Schmitt and O. Blanck contributed equally to the manuscript. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00066-020-01583-2) contains supplementary material, which is available to authorized users. Daniela Schmitt, PhD Daniela.Schmitt@med.uni-heidelberg.de 1 Klinik für Radioonkologie und Strahlentherapie, National Center for Radiation Research in Oncology (NCRO), Heidelberger Institut für Radioonkologie (HIRO), Universitätsklinikum Heidelberg, Heidelberg, Germany 2 Klinik für Strahlentherapie, Universitätsklinikum Schleswig-Holstein, Kiel, Germany 3 Klinik für Strahlentherapie und Radioonkologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany 4 Abteilung für Medizinische Strahlenphysik und Universitätsklinik für Radio-Onkologie, Inselspital—Universitätsspital Bern, Universität Bern, Bern, Switzerland 5 Universitätsklinik für Strahlentherapie, Universitätsklinikum Magdeburg, Magdeburg, Germany 6 Klinik für Strahlentherapie und Radioonkologie, Universitätsmedizin Mannheim, Universität Heidelberg, Mannheim, Germany 7 Klinik für Strahlentherapie und Onkologie, Universitätsklinikum Frankfurt, Frankfurt am Main, Germany 8 Institut für Computational Neuroscience, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany 9 Klinik für Strahlentherapie, Universitätsmedizin Rostock, Rostock, Germany 10 Klinik für Radioonkologie, CyberKnifeund Strahlentherapie, Universitätsklinikum Köln, Cologne, Germany 11 Klinik für Strahlentherapie—Radioonkologie, Universitätsklinikum Münster, Münster, Germany
A Study on Air Pollution Trends in Sanathnagar, Hyderabad using Principle Component and Cluster Analysis

Principle Component Analysis (PCA) and Cluster Analysis (CA) are used to understand the correlations among various air pollutants and the meteorological parameters in an industrial and commercial area at Hyderabad, India. The analysis is done using a decade long data and the correlations are obtained seasonally. The results from the both analysis have shown similar trends. The KMO test sampling adequacy and the Bartlett test significance values have proved that the results of analysis are satisfactory.
Kipple : Towards accessible, robust malware classification

The past few decades have shown that machine learning (ML) can be a powerful tool for static malware detection, with papers today still purporting to eek out slight accuracy improvements. At the same time, researchers have noted that ML-based classifiers are susceptible to adversarial ML , whereby attackers can exploit underlying weaknesses in ML techniques to specifically tailor their malware to evade these classifiers. Defending against these kinds of attacks has proven challenging, particularly for those not steeped in the field. To help tighten this gap, we have developed Kipple , a Windows Portable Executable (PE) malware classifier designed to detect attempts at evasion. Kipple uses a portfolio of classifiers, building on a primary classifier designed to detect “normal” malware by attaching classifiers designed to specific types of adversarial malware to it. While simplistic, this approach is able to make it significantly harder – though not impossible – to bypass the primary classifier. This paper reports on our process developing Kipple, specifically highlighting our methodology and several notable conclusions, including how our ensemble approach outperforms one using simple adversarial retraining and other performance notes. Our hope with publishing this paper is to provide an example defense against adversarial malware, and to also more broadly make the field more accessible to newcomers; towards this larger goal, we include a set of “lessons learned” for newcomers to the field, as well as an open-source software release containing Kipple’s models, the data it was built from, and various scripts used to help generate it.
Eﬃcient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation

Inverse reinforcement learning (IRL) methods attempt to recover the reward function of an agent by observing its behavior. Given the large amount of uncertainty in the underlying reward function, it is often useful to model this function probabilistically, rather than estimate a single reward function. However, existing Bayesian approaches to IRL use a Q-value function to approximate the likelihood, leading to a computationally intractable and inﬂexible framework. Here, we introduce kernel density Bayesian IRL (KD-BIRL), a method that uses kernel density estimation to approximate the likelihood, or the probability of the observed states and actions given a reward function. This approximation allows for eﬃcient posterior inference of the reward function given a sequence of agent observations. Empirically, using both linear and nonlinear reward functions in a Gridworld environment, we demonstrate that the KD-BIRL posterior centers around the true reward function.
Microsoft Word-9352040-file00

CNS and Pain Innovative Medicines DMPK, AstraZeneca R&D, Södertälje, Sweden (PL, JL, UF, IS, JJ, SB, JH, and LA); Cardiovascular and Metabolic Diseases Innovative Medicines DMPK, AstraZeneca R&D, Mölndal, Sweden (PL and TBA); Department of Pharmacy, Uppsala University, Uppsala, Sweden (PL); and Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden (TBA) DMD Fast Forward. Published on January 6, 2014 as doi:10.1124/dmd.113. 054155
Digital technologies in Smart farming: resistances and changes in Digital technologies in Smart farming: resistances and changes in the organization of agricultural industry the organization of agricultural industry

Digital technologies are essential in the agricultural industry, and they affect organizational changes. Indeed, they modify the way of working of the agricultural industry, which reviews their rules. Digital technologies transform the agricultural industry, introduce innovative knowledge, and improve business performance. The innovation has new challenges involving agriculture consult-ants and farmers. Furthermore, it introduces new techniques and devices to create value and profitability. These digital technologies impact the agricultural industry, such as to improve business performance and environmental impacts. Some studies talk about organizational changes in the agricultural industry, but there is not yet a substantial contribution of the literature that proposed to identify the main changes and organizational resistances in the agricultural industry. For this reason, we have decided to focus on the organizational changes and resistances resulting from digitization in the agricultural industry. The paper aims to show the concepts of smart farming and digital technologies, how the agricultural industry uses them, and their impacts. This paper contributes to a literature review regarding digital technologies in smart farming based on these concepts. We try to identify the main organizational changes and resistances generated using digital technologies. We focus on a theoretical analysis of the changes and resistances caused by digitalization in the industries and agricultural industries.
Exploration via Empowerment Gain: Combining Novelty, Surprise and Learning Progress

Exploration in the absence of a concrete task is a key characteristic of autonomous agents and vital for the emergence of intelligent behaviour. Various intrinsic motivation frameworks have been suggested, such as novelty seeking, surprise max-imisation or empowerment. Here we focus on the latter, empowerment, an agent-centric and information-theoretic measure of an agent’s perceived inﬂuence on the world. By considering improving one’s empowerment estimator – we call it empowerment gain (EG) – we derive a novel exploration criterion that focuses directly on the desired goal: exploration in order to help the agent recognise its capability to interact with the world. We propose a new theoretical framework based on improving a parametrised estimation of empowerment and show how it integrates novelty, surprise and learning progress into a single formulation. Empirically, we validate our theoretical ﬁndings on some simple but instructive grid world environments. We show that while such an agent is still novelty seeking, i. e. interested in exploring the whole state space, it focuses on exploration where its perceived inﬂuence is greater, avoiding areas of greater stochasticity or traps that limit its control.
Efek Antifertilitas Ekstrak Air dari Biji Carica papaya terhadap Keteraturan Siklus Estrus Mencit (Mus musculus L.)

Penellitian ini bertujuan untuk mengevaluasi efek antifertilitas ekstrak air biji pepaya dalam mempengaruhi keteraturan siklus estrus mencit. Mencit strain Swiss Webster betina dara digunakan sebagai hewan uji. Hewan uji dibagi dalam 5 kelompok perlakuan, yaitu K(-), bahan perlakuan berupa akuades; K(+), bahan perlakuan sediaan pil kontrasepsi merk X dengan dosis ; P1, bahan perlakuan ekstrak air biji pepaya dengan dosis 1,4 mg/ekor/hari; P2, bahan perlakuan ekstrak air biji pepaya dengan dosis 3,5 mg/ekor/hari; P3, bahan perlakuan ekstrak air biji pepaya dengan dosis 1,4 mg/ekor/hari. Masing-masing kelompok perlakuan diulang 7 kali. Perlakuan diberikan selama 21 hari. Pengamatan siklus estrus dilakukan dengan membuat sediaan apus vagina dengan pewarnaan Giemsa, setiap 5 hari. Persentase keteraturan siklus estrus ditentukan dengan menghitung jumah hewan yang mengalami siklus estrus teratur dibandingkan jumlah seluruh hewan uji pada kelompok perlakuan yang sama. Efek penghambatan siklus estrus esktrak air dari biji pepaya ditunjukkan pada P2 dan P3 yang memiliki keteraturan siklus estrus 57.1 dan 42.8%. Hasil penelitian ini membuktikan bahwa paparan esktrak air dari biji pepaya menunjukkan efek antifertilitas dengan mempengaruhi siklus estrus hewan uji. Kata kunci : C. papaya; antifertilitas; siklus estrus
Audio Injection Adversarial Example Attack

We study the problem of audio adversarial example attacks with sparse perturbations. Compared with image adversarial example attacks, attacking audio is more challenging because the audio structure is more complex and the perturbation is difﬁ-cult to conceal. To overcome this challenge, we propose an audio injection adversarial example attack, which provides a new sight light to increase the concealment of attack behavior. Experiments demonstrate that the proposed audio injection adversarial example attack can signiﬁcantly reduce the perturbation proportion and achieve a better attack effect than traditional attack methods.