scholarly journals An empirical investigation of command-line customization

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Michael Schröder ◽  
Jürgen Cito
Keyword(s):  
Knowledge Discovery ◽  
Empirical Investigation ◽  
Command Line ◽  
Learning Approaches ◽  
Collective Knowledge ◽  
User Experiences ◽  
Wide Range ◽  
Software Professionals ◽  
Insight Into

AbstractThe interactive command line, also known as the shell, is a prominent mechanism used extensively by a wide range of software professionals (engineers, system administrators, data scientists, etc.). Shell customizations can therefore provide insight into the tasks they repeatedly perform, how well the standard environment supports those tasks, and ways in which the environment could be productively extended or modified. To characterize the patterns and complexities of command-line customization, we mined the collective knowledge of command-line users by analyzing more than 2.2 million shell alias definitions found on GitHub. Shell aliases allow command-line users to customize their environment by defining arbitrarily complex command substitutions. Using inductive coding methods, we found three types of aliases that each enable a number of customization practices: Shortcuts (for nicknaming commands, abbreviating subcommands, and bookmarking locations), Modifications (for substituting commands, overriding defaults, colorizing output, and elevating privilege), and Scripts (for transforming data and chaining subcommands). We conjecture that identifying common customization practices can point to particular usability issues within command-line programs, and that a deeper understanding of these practices can support researchers and tool developers in designing better user experiences. In addition to our analysis, we provide an extensive reproducibility package in the form of a curated dataset together with well-documented computational notebooks enabling further knowledge discovery and a basis for learning approaches to improve command-line workflows.

scholarly journals Prediction of prokaryotic transposases from protein features with machine learning approaches

2021 ◽  
Vol 7 (7) ◽  
Author(s):  
Qian Wang ◽  
Jun Ye ◽  
Teng Xu ◽  
Ning Zhou ◽  
Zhongqiu Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antibiotic Resistance ◽  
Mutual Information ◽  
Ensemble Classifier ◽  
Training Dataset ◽  
Command Line ◽  
Learning Approaches ◽  
Command Line Tool ◽  
Selection Operator ◽  
Insight Into

Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea using machine learning (ML) approaches. We extracted a total of 2751 protein features from the training dataset including 14852 Tnps and 14852 controls, and selected 75 features as predictive signatures using the combined mutual information and least absolute shrinkage and selection operator algorithms. By aggregating these signatures, an ensemble classifier that integrated a collection of individual ML-based classifiers, was developed to identify Tnps. Further validation revealed that this classifier achieved good performance with an average AUC of 0.955, and met or exceeded other common methods. Based on this ensemble classifier, a stand-alone command-line tool designated TnpDiscovery was established to maximize the convenience for bioinformaticians and experimental researchers toward Tnp prediction. This study demonstrates the effectiveness of ML approaches in identifying Tnps, facilitating the discovery of novel Tnps in the future.

Sentence-Based Experience Logging in New Hearing Aid Users

2020 ◽  
Vol 29 (3S) ◽  
pp. 631-637
Author(s):  
Katja Lund ◽  
Rodrigo Ordoñez ◽  
Jens Bo Nielsen ◽  
Dorte Hammershøi
Keyword(s):  
Hearing Aid ◽  
Patient Experiences ◽  
Online Tool ◽  
Rehabilitation Process ◽  
Hearing Rehabilitation ◽  
Clinical Observations ◽  
Hearing Aid Use ◽  
Wide Range ◽  
Insight Into ◽  
Shed Light

Purpose The aim of this study was to develop a tool to gain insight into the daily experiences of new hearing aid users and to shed light on aspects of aided performance that may not be unveiled through standard questionnaires. Method The tool is developed based on clinical observations, patient experiences, expert involvement, and existing validated hearing rehabilitation questionnaires. Results An online tool for collecting data related to hearing aid use was developed. The tool is based on 453 prefabricated sentences representing experiences within 13 categories related to hearing aid use. Conclusions The tool has the potential to reflect a wide range of individual experiences with hearing aid use, including auditory and nonauditory aspects. These experiences may hold important knowledge for both the patient and the professional in the hearing rehabilitation process.

scholarly journals Engineering the Interface between Inorganic Nanoparticles and Biological Systems through Ligand Design

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1001
Author(s):  
Rui Huang ◽  
David C. Luther ◽  
Xianzhi Zhang ◽  
Aarohi Gupta ◽  
Samantha A. Tufts ◽  
...  
Keyword(s):  
Organic Synthesis ◽  
Molecular Design ◽  
Biological Systems ◽  
Ligand Design ◽  
Inorganic Nanoparticles ◽  
Organic Ligands ◽  
Biological Applications ◽  
Wide Range ◽  
Insight Into

Nanoparticles (NPs) provide multipurpose platforms for a wide range of biological applications. These applications are enabled through molecular design of surface coverages, modulating NP interactions with biosystems. In this review, we highlight approaches to functionalize nanoparticles with ”small” organic ligands (Mw < 1000), providing insight into how organic synthesis can be used to engineer NPs for nanobiology and nanomedicine.

scholarly journals Gas Damping in Capacitive MEMS Transducers in the Free Molecular Flow Regime

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2566
Author(s):  
Boris A. Boom ◽  
Alessandro Bertolini ◽  
Eric Hennes ◽  
Johannes F. J. van den Brand
Keyword(s):  
Flow Regime ◽  
Gas Diffusion ◽  
Diffusion Time ◽  
Molecular Flow ◽  
Free Molecular Flow ◽  
Simple Analytical Model ◽  
Gas Damping ◽  
Wide Range ◽  
Capacitive Mems ◽  
Insight Into

We present a novel analysis of gas damping in capacitive MEMS transducers that is based on a simple analytical model, assisted by Monte-Carlo simulations performed in Molflow+ to obtain an estimate for the geometry dependent gas diffusion time. This combination provides results with minimal computational expense and through freely available software, as well as insight into how the gas damping depends on the transducer geometry in the molecular flow regime. The results can be used to predict damping for arbitrary gas mixtures. The analysis was verified by experimental results for both air and helium atmospheres and matches these data to within 15% over a wide range of pressures.

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

scholarly journals Securitisation gaps: Towards ideational understandings of state weakness

2021 ◽  
pp. 1-20
Author(s):  
Kevork Oskanian
Keyword(s):  
The State ◽  
Collective Knowledge ◽  
State Action ◽  
Role Of The State ◽  
Wide Range ◽  
State And Society ◽  
Interpretive Approach ◽  
Recent Developments ◽  
The Republic

Abstract This article contributes a securitisation-based, interpretive approach to state weakness. The long-dominant positivist approaches to the phenomenon have been extensively criticised for a wide range of deficiencies. Responding to Lemay-Hébert's suggestion of a ‘Durkheimian’, ideational-interpretive approach as a possible alternative, I base my conceptualisation on Migdal's view of state weakness as emerging from a ‘state-in-society's’ contested ‘strategies of survival’. I argue that several recent developments in Securitisation Theory enable it to capture this contested ‘collective knowledge’ on the state: a move away from state-centrism, the development of a contextualised ‘sociological’ version, linkages made between securitisation and legitimacy, and the acknowledgment of ‘securitisations’ as a contested Bourdieusian field. I introduce the concept of ‘securitisation gaps’ – divergences in the security discourses and practices of state and society – as a concept aimed at capturing this contested role of the state, operationalised along two logics (reactive/substitutive) – depending on whether they emerge from securitisations of the state action or inaction – and three intensities (latent, manifest, and violent), depending on the extent to which they involve challenges to state authority. The approach is briefly illustrated through the changing securitisation gaps in the Republic of Lebanon during the 2019–20 ‘October Uprising’.

scholarly journals Prediction of Dead Oil Viscosity: Machine Learning vs. Classical Correlations

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 930
Author(s):  
Fahimeh Hadavimoghaddam ◽  
Mehdi Ostadhassan ◽  
Ehsan Heidaryan ◽  
Mohammad Ali Sadri ◽  
Inna Chapanova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Viscosity Data ◽  
Oil Viscosity ◽  
Pvt Data ◽  
Proposed Model ◽  
Wide Range ◽  
Engineering Problems ◽  
Functional Forms ◽  
Insight Into

Dead oil viscosity is a critical parameter to solve numerous reservoir engineering problems and one of the most unreliable properties to predict with classical black oil correlations. Determination of dead oil viscosity by experiments is expensive and time-consuming, which means developing an accurate and quick prediction model is required. This paper implements six machine learning models: random forest (RF), lightgbm, XGBoost, multilayer perceptron (MLP) neural network, stochastic real-valued (SRV) and SuperLearner to predict dead oil viscosity. More than 2000 pressure–volume–temperature (PVT) data were used for developing and testing these models. A huge range of viscosity data were used, from light intermediate to heavy oil. In this study, we give insight into the performance of different functional forms that have been used in the literature to formulate dead oil viscosity. The results show that the functional form f(γAPI,T), has the best performance, and additional correlating parameters might be unnecessary. Furthermore, SuperLearner outperformed other machine learning (ML) algorithms as well as common correlations that are based on the metric analysis. The SuperLearner model can potentially replace the empirical models for viscosity predictions on a wide range of viscosities (any oil type). Ultimately, the proposed model is capable of simulating the true physical trend of the dead oil viscosity with variations of oil API gravity, temperature and shear rate.

A new species of Sclerocephalus with a fully ossified endocranium gives insight into braincase evolution in temnospondyls

2021 ◽  
pp. 1-13
Author(s):  
Rainer R. Schoch ◽  
Gabriela Sobral
Keyword(s):  
New Species ◽  
Character Evolution ◽  
Late Paleozoic ◽  
New Taxon ◽  
Early Permian ◽  
Top Predator ◽  
Central European ◽  
Wide Range ◽  
A New Species ◽  
Insight Into

Abstract The late Paleozoic temnospondyl Sclerocephalus formed an aquatic top predator in various central European lakes of the late Carboniferous and early Permian. Despite hundreds of specimens spanning a wide range of sizes, knowledge of the endocranium (braincase and palatoquadrate) remained very insufficient in Sclerocephalus and other stereospondylomorphs because even large skulls had unossified endocrania. A new specimen from a stratigraphically ancient deposit at St. Wendel in southwestern Germany is recognized as representing a new taxon, S. concordiae new species, and reveals a completely ossified endocranium. The sphenethmoid was completely ossified from the basisphenoid to the anterior ethmoid region, co-ossified with the parasphenoid, and the basipterygoid joint was fully established. The pterygoid bears a slender, S-shaped epipterygoid, which formed a robust pillar lateral to the braincase. The massive stapes was firmly sutured to the parasphenoid. In the temnospondyl endocranium, character evolution involved various changes in the epipterygoid region, which evolved distinct morphologies in each of the major clades. UUID: http://zoobank.org/5e6d2078-eacf-4467-84cf-a12efcae7c0b

scholarly journals An Insight into Saponins from Quinoa (Chenopodium quinoa Willd): A Review

Molecules ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 1059 ◽  
Author(s):  
Khadija El Hazzam ◽  
Jawhar Hafsa ◽  
Mansour Sobeh ◽  
Manal Mhada ◽  
Moha Taourirte ◽  
...  
Keyword(s):  
Heat Treatment ◽  
Bitter Taste ◽  
Biological Activities ◽  
Chenopodium Quinoa ◽  
Toxic Effects ◽  
Mechanical Abrasion ◽  
Important Group ◽  
The Past ◽  
Wide Range ◽  
Insight Into

Saponins are an important group found in Chenopodium quinoa. They represent an obstacle for the use of quinoa as food for humans and animal feeds because of their bitter taste and toxic effects, which necessitates their elimination. Several saponins elimination methods have been examined to leach the saponins from the quinoa seeds; the wet technique remains the most used at both laboratory and industrial levels. Dry methods (heat treatment, extrusion, roasting, or mechanical abrasion) and genetic methods have also been evaluated. The extraction of quinoa saponins can be carried out by several methods; conventional technologies such as maceration and Soxhlet are the most utilized methods. However, recent research has focused on technologies to improve the efficiency of extraction. At least 40 saponin structures from quinoa have been isolated in the past 30 years, the derived molecular entities essentially being phytolaccagenic, oleanolic and serjanic acids, hederagenin, 3β,23,30 trihydroxy olean-12-en-28-oic acid, 3β-hydroxy-27-oxo-olean-12en-28-oic acid, and 3β,23,30 trihydroxy olean-12-en-28-oic acid. These metabolites exhibit a wide range of biological activities, such as molluscicidal, antifungal, anti-inflammatory, hemolytic, and cytotoxic properties.

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

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