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2022 ◽  
Vol 14 (1) ◽  
pp. 1-27
Author(s):  
Khalid Belhajjame

Workflows have been adopted in several scientific fields as a tool for the specification and execution of scientific experiments. In addition to automating the execution of experiments, workflow systems often include capabilities to record provenance information, which contains, among other things, data records used and generated by the workflow as a whole but also by its component modules. It is widely recognized that provenance information can be useful for the interpretation, verification, and re-use of workflow results, justifying its sharing and publication among scientists. However, workflow execution in some branches of science can manipulate sensitive datasets that contain information about individuals. To address this problem, we investigate, in this article, the problem of anonymizing the provenance of workflows. In doing so, we consider a popular class of workflows in which component modules use and generate collections of data records as a result of their invocation, as opposed to a single data record. The solution we propose offers guarantees of confidentiality without compromising lineage information, which provides transparency as to the relationships between the data records used and generated by the workflow modules. We provide algorithmic solutions that show how the provenance of a single module and an entire workflow can be anonymized and present the results of experiments that we conducted for their evaluation.


Author(s):  
Ayman Elgharabawy ◽  
Mukesh Prasad ◽  
Chin-Teng Lin

Equality and incomparability multi-label ranking have not been introduced to learning before. This paper proposes new native ranker neural network to address the problem of multi-label ranking including incomparable preference orders using a new activation and error functions and new architecture. Preference Neural Network PNN solves the multi-label ranking problem, where labels may have indifference preference orders or subgroups which are equally ranked. PNN is a nondeep, multiple-value neuron, single middle layer and one or more output layers network. PNN uses a novel positive smooth staircase (PSS) or smooth staircase (SS) activation function and represents preference orders and Spearman ranking correlation as objective functions. It is introduced in two types, Type A is traditional NN architecture and Type B uses expanding architecture by introducing new type of hidden neuron has multiple activation function in middle layer and duplicated output layers to reinforce the ranking by increasing the number of weights. PNN accepts single data instance as inputs and output neurons represent the number of labels and output value represents the preference value. PNN is evaluated using a new preference mining data set that contains repeated label values which have not experimented on before. SS and PS speed-up the learning and PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency.


2021 ◽  
Vol 14 (1) ◽  
pp. 50
Author(s):  
Haiqing He ◽  
Jing Yu ◽  
Penggen Cheng ◽  
Yuqian Wang ◽  
Yufeng Zhu ◽  
...  

Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.


F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1264
Author(s):  
Nisha Kumari Devaraj ◽  
Ameer Al Mubarak Hamzah

Background: Since adsorption is a complex process, numerous models and theories have been devised to gain general understanding of its underlying mechanisms. The interaction between the adsorbates and adsorbents can be identified via modelling of the adsorption data with different adsorption isotherms as well as kinetic models. Many studies are also focused on developing predictive modelling techniques to facilitate accurate prediction of future adsorption trends. Methods: In this study, a predictive model was developed based on a multiple linear regression technique using existing data of As(V) adsorption onto several coated and uncoated magnetite samples. To understand the mechanisms and interactions involved, the data was first modelled using either Temkin or Freundlich linear isotherms.  The predicted value is a single data point extension from the training data set. Subsequently, the predicted outcome and the experimental values were compared using multiple error functions to assess the predictive model’s performance. Results: In addition, certain values were compared to that obtained from the literature, and the results were found to have low error margins. Conclusion: To further gauge the effectiveness of the proposed model in accurately predicting future adsorption trends, it should be further tested on different adsorbent and adsorbate combinations.


Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Davide Giavarina ◽  
Mariarosa Carta

Abstract Objectives A few CLIA automated immunoassays for the recognition of anti S1-RBD SARS-CoV-2 antibodies have recently been placed on the market. Preliminary data demonstrate a high correlation between methods but wide differences in absolute concentrations. A new WHO international standard for anti-SARS-CoV-2 immunoglobulin, NIBSC code 20/136, has been recently introduced to reduce the differences. The aim of this study is thus to verify the harmonization made by NIBSC 20/136 on Ab anti S1-RBD measurement on real samples. Methods The following assays were studied: LIAISON® SARS-CoV-2 TrimericS IgG (DiaSorin); Elecsys® anti-SARS-CoV-2 S (ROCHE); Atellica IM SARS-CoV-2 IgG (sCOVG) (Siemens); MAGLUMI® SARS-CoV-2 S-RBD IgG (Snibe), measuring 210 samples from 70 health workers with no previous SARS-CoV2 infection, during their Pfizer-BioNTech’s BNT162b2 vaccination period. Results The recalculation of concentrations based on the NIBSC 20/136 standardization improve the analytical and diagnostic comparability but do not cancel this variability between methods: recalibrated results remain different across methods, both in terms of tendency and single data. Conclusions The recalculation of concentrations based on the NIBSC 20/136 standardization improves the analytical and diagnostic comparability but does not cancel the differences between methods: recalibrated results remain different across methods, both in terms of tendency and single data.


Author(s):  
Neelanjan Manna

Abstract: Nowadays we use text passwords to encrypt a file. This research paper proposes to use multimedia files like images videos, audio files and even applications as the password key to encrypt sensitive information. This algorithm can encrypt bulk data as well as single data sets. Keywords: steganography, multimedia file as key, Quantum computer, cryptography, Quantum computer proof encryption.


Author(s):  
Kamala Adhikari ◽  
Scott B Patten ◽  
Alka B Patel ◽  
Shahirose Premji ◽  
Suzanne Tough ◽  
...  

Data pooling from pre-existing multiple datasets can be useful to increase study sample size and statistical power to answer a research question. However, individual datasets may contain variables that measure the same construct differently, posing challenges for data pooling. Variable harmonization, an approach that can generate comparable datasets from heterogeneous sources, can address this issue in some circumstances. As an illustrative example, this paper describes the data harmonization strategies that helped generate comparable datasets across two Canadian pregnancy cohort studies– the All Our Families and the Alberta Pregnancy Outcomes and Nutrition. Variables were harmonized considering multiple features across the datasets: the construct measured; question asked/response options; the measurement scale used; the frequency of measurement; timing of measurement, and the data structure. Completely matching, partially matching, and completely un-matching variables across the datasets were determined based on these features. Variables that were an exact match were pooled as is. Partially matching variables were synchronized across the datasets considering the frequency of measurement, the timing of measurement, and response options. Variables that were completely unmatching could not be harmonized into a single variable. The variable harmonization strategies that were used to generate comparable cohort datasets for data pooling are applicable to other data sources. Future studies may employ or evaluate these strategies. Variable harmonization and pooling provide an opportunity to increase study power and the utility of existing data, permitting researchers to answer novel research questions in a statistically efficient, timely, and cost-efficient manner that could not be achieved using a single data source.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xingyu Zhou ◽  
Zhuangwei Kang ◽  
Robert Canady ◽  
Shunxing Bao ◽  
Daniel Allen Balasubramanian ◽  
...  

Deep learning has shown impressive performance acrosshealth management and prognostics applications. Nowadays, an emerging trend of machine learning deployment on resource constraint hardware devices like micro-controllers(MCU) has aroused much attention. Given the distributed andresource constraint nature of many PHM applications, using tiny machine learning models close to data source sensors for on-device inferences would be beneficial to save both time andadditional hardware resources. Even though there has beenpast works that bring TinyML on MCUs for some PHM ap-plications, they are mainly targeting single data source usage without higher-level data incorporation with cloud computing.We study the impact of potential cooperation patterns betweenTinyML on edge and more powerful computation resources oncloud and how this would make an impact on the application patterns in data-driven prognostics. We introduce potential ap-plications where sensor readings are utilized for system health status prediction including status classification and remaining useful life regression. We find that MCUs and cloud com-puting can be adaptive to different kinds of machine learning models and combined in flexible ways for diverse requirement.Our work also shows limitations of current MCU-based deep learning in data-driven prognostics And we hope our work can


Earth ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 1006-1037
Author(s):  
Diana Contreras ◽  
Sean Wilkinson ◽  
Philip James

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting data on building damage and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 39 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by these data. We conclude that modern reconnaissance missions cannot rely on a single data source. Different data sources should complement each other, validate collected data or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important data source.


Author(s):  
Jens Trautmann ◽  
Arthur Beckers ◽  
Lennert Wouters ◽  
Stefan Wildermann ◽  
Ingrid Verbauwhede ◽  
...  

Locating a cryptographic operation in a side-channel trace, i.e. finding out where it is in the time domain, without having a template, can be a tedious task even for unprotected implementations. The sheer amount of data can be overwhelming. In a simple call to OpenSSL for AES-128 ECB encryption of a single data block, only 0.00028% of the trace relate to the actual AES-128 encryption. The rest is overhead. We introduce the (to our best knowledge) first method to locate a cryptographic operation in a side-channel trace in a largely automated fashion. The method exploits meta information about the cryptographic operation and requires an estimate of its implementation’s execution time.The method lends itself to parallelization and our implementation in a tool greatly benefits from GPU acceleration. The tool can be used offline for trace segmentation and for generating a template which can then be used online in real-time waveformmatching based triggering systems for trace acquisition or fault injection. We evaluate it in six scenarios involving hardware and software implementations of different cryptographic operations executed on diverse platforms. Two of these scenarios cover realistic protocol level use-cases and demonstrate the real-world applicability of our tool in scenarios where classical leakage-detection techniques would not work. The results highlight the usefulness of the tool because it reliably and efficiently automates the task and therefore frees up time of the analyst.The method does not work on traces of implementations protected by effective time randomization countermeasures, e.g. random delays and unstable clock frequency, but is not affected by masking, shuffling and similar countermeasures.


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