scholarly journals XRF and XRPD data sets in ternary mixtures with high level micro-absorption and/or preferred orientations problems for phase quantification analysis

Data in Brief ◽  
2021 ◽  
pp. 107043
Author(s):  
Beatrice Mangolini ◽  
Luca Palin ◽  
Marco Milanesio ◽  
Mattia Lopresti
Keyword(s):  
Ternary Mixtures ◽  
Data Sets ◽  
Quantification Analysis ◽  
Phase Quantification ◽  
High Level

scholarly journals EFFECTIVENESS OF GUIDANCE AND COUNSELING SERVICES BY USING THE APPLICATIONS INSTRUMENTATION AND ACTIVITY DATA SET IN SMA NEGERI 1 METRO LESSON 2009/2010

2014 ◽  
Vol 4 (1) ◽  
pp. 35
Author(s):  
Agus Wibowo
Keyword(s):  
Qualitative Research ◽  
Counseling Services ◽  
Guidance And Counseling ◽  
Data Sets ◽  
Research Subjects ◽  
Activity Data ◽  
Data Set ◽  
Research Level ◽  
Data Collection Technique ◽  
High Level

Abstract: Implementation of guidance and counseling services should be based on the needs and problems of students, so the effectiveness of the service will be achieved to the fullest. But the reality is a lot of implementation of guidance and counseling services in schools, do not notice it. So that the completion of the problems experienced by students sama.Berangkat always use the services of this, the research level of effectiveness of guidance and counseling that implementation has been using the application activity instrumentation and data sets as the basis for an implementation of the service. The method used is a qualitative research subjects that teachers BK and Students at SMA Negeri 1 Metro. Data collection technique through interview, observation and documentation. Research results show that by utilizing activity instrumentation applications and data sets, the counseling services have a high level of effectiveness. In carrying out the service, BK teachers can identify problems and needs experienced by students, so that the efforts of the assistance provided to be more precise, and problem students can terentaskan optimally.Keyword: Guidance and Counseling, Instrumentation Applications, Data Association

scholarly journals An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Image Classification ◽  
Image Data ◽  
Data Sets ◽  
Automatic Feature Extraction ◽  
Terminal Set ◽  
Comparable Performance ◽  
International Publishing ◽  
High Level

© Springer International Publishing AG, part of Springer Nature 2018. Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.

scholarly journals Hemodynamic evaluation of patients with Moyamoya Angiopathy: comparison of resting-state fMRI to breath-hold fMRI and [15O]water PET

2021 ◽  
Author(s):  
Leonie Zerweck ◽  
Constantin Roder ◽  
Till-Karsten Hauser ◽  
Johannes Thurow ◽  
Annerose Mengel ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Cerebrovascular Reactivity ◽  
Breath Hold ◽  
Data Sets ◽  
Moyamoya Angiopathy ◽  
Perfusion Reserve ◽  
Hemodynamic Evaluation ◽  
Patient Cooperation ◽  
High Level

Abstract Purpose Patients with Moyamoya Angiopathy (MMA) require hemodynamic evaluation to assess the risk of stroke. Assessment of cerebral blood flow with [15O]water PET and acetazolamide challenge is the diagnostic standard for the evaluation of the cerebral perfusion reserve (CPR). Estimation of the cerebrovascular reactivity (CVR) by use of breath-hold-triggered fMRI (bh-fMRI) as an index of CPR has been proposed as a reliable and more readily available approach. Recent findings suggest the use of resting-state fMRI (rs-fMRI) which requires minimum patient compliance. The aim of this study was to compare rs-fMRI to bh-fMRI and [15O]water PET in patients with MMA. Methods Patients with MMA underwent rs-fMRI and bh-fMRI in the same MRI session. Maps of the CVR gained by both modalities were compared retrospectively by calculating the correlation between the mean CVR of 12 volumes of interest. Additionally, the rs-maps of a subgroup of patients were compared to CPR-maps gained by [15O]water PET. Results The comparison of the rs-maps and the bh-maps of 24 patients revealed a good correlation (Pearson’s r = 0.71 ± 0.13; preoperative patients: Pearson’s r = 0.71 ± 0.17; postoperative patients: Pearson’s r = 0.71 ± 0.11). The comparison of 7 rs-fMRI data sets to the corresponding [15O]water PET data sets also revealed a high level of agreement (Pearson’s r = 0.80 ± 0.19). Conclusion The present analysis indicates that rs-fMRI might be a promising non-invasive method with almost no patient cooperation needed to evaluate the CVR. Further prospective studies are required.

scholarly journals The reproducibility challenge – what researchers need

2017 ◽  
Author(s):  
Federica Rosetta
Keyword(s):  
Scientific Discovery ◽  
Open Data ◽  
Science Research ◽  
Open Science ◽  
Research Data ◽  
Data Sets ◽  
Scientific Practices ◽  
Data Citation ◽  
Materials Used ◽  
High Level

Watch the VIDEO here.Within the Open Science discussions, the current call for “reproducibility” comes from the raising awareness that results as presented in research papers are not as easily reproducible as expected, or even contradicted those original results in some reproduction efforts. In this context, transparency and openness are seen as key components to facilitate good scientific practices, as well as scientific discovery. As a result, many funding agencies now require the deposit of research data sets, institutions improve the training on the application of statistical methods, and journals begin to mandate a high level of detail on the methods and materials used. How can researchers be supported and encouraged to provide that level of transparency? An important component is the underlying research data, which is currently often only partly available within the article. At Elsevier we have therefore been working on journal data guidelines which clearly explain to researchers when and how they are expected to make their research data available. Simultaneously, we have also developed the corresponding infrastructure to make it as easy as possible for researchers to share their data in a way that is appropriate in their field. To ensure researchers get credit for the work they do on managing and sharing data, all our journals support data citation in line with the FORCE11 data citation principles – a key step in the direction of ensuring that we address the lack of credits and incentives which emerged from the Open Data analysis (Open Data - the Researcher Perspective https://www.elsevier.com/about/open-science/research-data/open-data-report ) recently carried out by Elsevier together with CWTS. Finally, the presentation will also touch upon a number of initiatives to ensure the reproducibility of software, protocols and methods. With STAR methods, for instance, methods are submitted in a Structured, Transparent, Accessible Reporting format; this approach promotes rigor and robustness, and makes reporting easier for the author and replication easier for the reader.

scholarly journals Human Gaze Assisted Artificial Intelligence: A Review

2020 ◽  
Author(s):  
Ruohan Zhang ◽  
Akanksha Saran ◽  
Bo Liu ◽  
Yifeng Zhu ◽  
Sihang Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Cognitive State ◽  
Data Sets ◽  
Common Goal ◽  
Research Areas ◽  
Future Challenges ◽  
Potential Applications ◽  
High Level ◽  
And Robotics

Human gaze reveals a wealth of information about internal cognitive state. Thus, gaze-related research has significantly increased in computer vision, natural language processing, decision learning, and robotics in recent years. We provide a high-level overview of the research efforts in these fields, including collecting human gaze data sets, modeling gaze behaviors, and utilizing gaze information in various applications, with the goal of enhancing communication between these research areas. We discuss future challenges and potential applications that work towards a common goal of human-centered artificial intelligence.

Influences of Embedding Parameters and Segment Sizes in Recursive Characteristics Analysis on Coefficients of Friction

2021 ◽  
Vol 31 (04) ◽  
pp. 2150058
Author(s):  
Guodong Sun ◽  
Chao Zhang ◽  
Hua Zhu ◽  
Shihui Lang
Keyword(s):  
Time Series ◽  
Dynamical Evolution ◽  
Chaotic Time Series ◽  
Computational Time ◽  
Data Sets ◽  
Self Similarity ◽  
Tribological Behaviors ◽  
Characteristics Analysis ◽  
Quantification Analysis ◽  
Recursive Matrix

The methods of recurrence plots (RPs) and recurrence quantification analysis (RQA) have been used to investigate the tribosystem. The morphology of RPs and RQA measures are strongly dependent on the embedding parameters of the recursive matrix and the segment sizes of the time-series. To improve the calculation accuracy of recursive characteristics analysis, the influences of the embedding parameters and segment sizes on the morphology of RPs and RQA measures have been studied in this letter. Three kinds of theoretical chaotic time-series and measured coefficient of friction (COF) signals during the running-in process were chosen as research objects, and the morphology of RPs and RQA measures were obtained using CRP toolbox afterward. The results indicate that no embedding was actually needed if the data sets are to be qualitatively analyzed using RPs and RQA. Additionally, the morphology of RPs and RQA measures are sensitive to the segment sizes for theoretical chaotic time-series, while the RQA measures of COF signal in the steady-state period are rather stable due to its self-similarity. Finally, according to the guidelines of the parameter settings, the dynamical evolution of measured COF signals during the running-in process have been investigated. It is indicated that recursive characteristics of COF signals could reveal the tribological behaviors’ evolution and conduct the running-in status identification. The results in this paper are significant for improving the calculation accuracy and saving computational time when using the method of recursive characteristics analysis on the tribological behaviors.

Big Data and Machine Learning

2018 ◽  
pp. 225-239
Author(s):  
Fernando Enrique Lopez Martinez ◽  
Edward Rolando Núñez-Valdez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Complete Solution ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Healthcare Organizations ◽  
High Level

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.

Chapter 17. Logic Tensor Networks: Theory and Applications

2021 ◽  
Author(s):  
Luciano Serafini ◽  
Artur d’Avila Garcez ◽  
Samy Badreddine ◽  
Ivan Donadello ◽  
Michael Spranger ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Data Sets ◽  
Symbolic Representations ◽  
Tensor Networks ◽  
Multiple Data ◽  
Abstract Knowledge ◽  
Symbolic Ai ◽  
Rich Data ◽  
Efficient Learning ◽  
High Level

The recent availability of large-scale data combining multiple data modalities has opened various research and commercial opportunities in Artificial Intelligence (AI). Machine Learning (ML) has achieved important results in this area mostly by adopting a sub-symbolic distributed representation. It is generally accepted now that such purely sub-symbolic approaches can be data inefficient and struggle at extrapolation and reasoning. By contrast, symbolic AI is based on rich, high-level representations ideally based on human-readable symbols. Despite being more explainable and having success at reasoning, symbolic AI usually struggles when faced with incomplete knowledge or inaccurate, large data sets and combinatorial knowledge. Neurosymbolic AI attempts to benefit from the strengths of both approaches combining reasoning with complex representation of knowledge and efficient learning from multiple data modalities. Hence, neurosymbolic AI seeks to ground rich knowledge into efficient sub-symbolic representations and to explain sub-symbolic representations and deep learning by offering high-level symbolic descriptions for such learning systems. Logic Tensor Networks (LTN) are a neurosymbolic AI system for querying, learning and reasoning with rich data and abstract knowledge. LTN introduces Real Logic, a fully differentiable first-order language with concrete semantics such that every symbolic expression has an interpretation that is grounded onto real numbers in the domain. In particular, LTN converts Real Logic formulas into computational graphs that enable gradient-based optimization. This chapter presents the LTN framework and illustrates its use on knowledge completion tasks to ground the relational predicates (symbols) into a concrete interpretation (vectors and tensors). It then investigates the use of LTN on semi-supervised learning, learning of embeddings and reasoning. LTN has been applied recently to many important AI tasks, including semantic image interpretation, ontology learning and reasoning, and reinforcement learning, which use LTN for supervised classification, data clustering, semi-supervised learning, embedding learning, reasoning and query answering. The chapter presents some of the main recent applications of LTN before analyzing results in the context of related work and discussing the next steps for neurosymbolic AI and LTN-based AI models.

scholarly journals Smoothing surfaces and attributes

2014 ◽  
Vol 33 (2) ◽  
pp. 128-129 ◽  
Author(s):  
Matt Hall
Keyword(s):  
Open Access ◽  
Programming Languages ◽  
Open Data ◽  
Data Sets ◽  
Proprietary Software ◽  
High Level

Welcome to this new column. Every two months, a geoscientist will present a brief exploration of a geophysical topic. The idea is to take a tour bus around a subject and point out some of the sights, perhaps stopping briefly at an exemplary problem or instructive viewpoint. So far it's useful, but maybe not remarkable. The remarkable thing, I hope, is that the tour will be open access. The tutors will use only open data sets that anyone can download. There will be no proprietary software. I will strongly encourage the use of Octave, R, or Python, all high-level (that is, easy-to-learn) programming languages for scientists, and the important parts of the code will be shared. I've tried to give a flavor of all this in today's tutorial, using Python. If you are new to Python, IPython is a great place to start—visit ipython.org/install .

Hybrid Probabilistic and Possibilistic Safety Assessment: Methodology and Application

2001 ◽  
Author(s):  
Kazuyuki Kato ◽  
Osamu Amano ◽  
Takao Ikeda ◽  
Hideji Yoshida ◽  
Hiroyasu Takase
Keyword(s):  
Safety Assessment ◽  
System Development ◽  
Probability Density Functions ◽  
Density Functions ◽  
Data Sets ◽  
Assessment Methodology ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
High Level ◽  
Support Decision Making

Abstract This paper presents a unified methodology to handle variability and ignorance by using probabilistic and possibilistic techniques respectively. The methodology has been applied to the safety assessment of geological disposal of high level radioactive waste. Ignorances associated with scenarios, models and parameters were defined in terms of fuzzy membership functions derived through a series of interviews to the experts, while variability was formulated by means of probability density functions (pdfs) based on available data sets. The exercise demonstrated the applicability of the new methodology and, in particular, its advantage in quantifying ignorances based on expert opinion and in providing information on the dependence of assessment results on the level of conservatism. In addition, it was shown that sensitivity analysis can identify key parameters contributing to uncertainties associated with results of the overall assessment. The information mentioned above can be utilized to support decision making and to guide the process of disposal system development and optimization of protection against potential exposure.

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