Miracle-dz/UniFAR-FTUs huggingface dataset:
Stitching of IR images into a single content based on data which analysis obtained by an optical camera

The acquisition of complex data of the recorded scene is an important task for video analysis of the processes occurring. The use of IR images allows obtaining data on additional characteristics that are not visible in the optical range. The data obtained by IR sensors can be in the near and far range, which makes it possible to see objects in the dark or to obtain data on their temperature. In the second case, the boundaries of objects are vague and difficult to correlate with the usual optical ranges. To do this a combination of data obtained by a pair of cameras is used. In this paper, we propose using the algorithm for stitching IR images based on data obtained in the optical range. To this end, an approach will be applied that includes parallel analysis of data on: saliency maps; search for boundaries and key points; reduction of bit resolution of images with preservation of borders; image matching; filtering data and restoring the sharpness of object boundaries. As an example, the result of combining data obtained under poor lighting conditions and the results of combining television images will be presented.
Towards Interactive Causal Relation Discovery Driven by an Ontology

Discovering causal relations in a knowledge base represents nowadays a challenging issue, as it gives a brand new way of understanding complex domains. In this paper, we present a method to combine an ontology with a probabilistic rela-tional model (PRM), in order to help a user to check his/her assumption on causal relations between data and to discover new relationships. This assumption is important as it guides the PRM construction and provide a learning under causal constraints.
Selective maintenance optimisation considering random common cause failures and imperfect maintenance

This article addresses a selective maintenance optimisation problem for systems subject to random common cause failures. A system is likely to suffer from several random common cause failures during a given mission. Random common cause events, which occur with a specific probability distribution, may result in the simultaneous failures of multiple elements. Because time is one of the most crucial maintenance resources, a time-based imperfect maintenance model is proposed to quantify the maintenance efficiency of each candidate maintenance action. To meet the demands of the next mission, a selective maintenance model is proposed to optimally identify a subset of maintenance activities to be performed on certain elements of a system. A genetic algorithm and Monte Carlo simulation method is presented to solve the proposed selective maintenance optimisation problem. Illustrative examples combined with detailed discussions are presented to demonstrate the effectiveness of the proposed model. The results show that the proposal of time-based imperfect maintenance model can yield better maintenance results, while ignoring random common cause failures in selective maintenance optimisation may produce biased maintenance decisions and system reliability.
Revisiting Effectiveness of Energy Conserving Opportunistic Transmission Schemes in Energy Harvesting Wireless Sensor Networks

Opportunistic transmission schemes improve the lifetime of conventional wireless sensor networks (WSNs) by reducing the number of transmissions. However, this comes at the expense of performance since fewer measurements are available. We show that in energy harvesting (EH) WSNs, in which the sensor nodes harvest energy from the environment, this trade-off is fundamentally different. For a general model in which the nodes experience independent and non-identical fading and the EH process at a node is stationary and ergodic, we present lower bounds on the mean squared error (MSE) for two important classes of channel-based opportunistic transmission schemes, namely, censoring and ordered transmissions. For the latter, we present two novel variants that arise depending on whether the energy in the battery of an EH sensor node is accounted for before ordering or not. For censoring, the lower bound leads to an insightful and explicit characterization of the optimum censoring threshold for each node. For ordered transmissions, it helps determine the optimal number of nodes that should be selected to transmit. We find that the ordered transmission schemes can outperform the censoring scheme. We also propose a hybrid scheme that combines the best features of the above schemes for EH WSNs.
AN ENSEMBLE MODEL FOR COLLABORATIVE FILTERING TO INVOLVE ALL ASPECTS OF DATASET

The accuracy of predictions is better if the combinations of the different approaches are used. Currently in collaborative filtering research, the linear blending of various methods is used. More accurate classifiers can be obtained by combining less accurate ones. This approach is called ensembles of classifiers. Different collaborative filtering methods uncover the different aspects of the dataset. Some of them are good at finding out local relationships; the others work for the global characterization of the data. Ensembles of different collaborative filtering algorithms can be created to provide more accurate recommender systems.
Analysis of Student Critical Thinking Ability in Colloid Chart with Problem-Based Learning Model

The ability to manage learning using a variety of methods, models and learning techniques is needed to improve teachers' knowledge and teaching skills. The ability to choose appropriate methods, models and learning techniques in the teaching and learning process will have an effect on the success of student learning. Problem-based learning model is a learning model that uses real-world problems as a context for learners to learn about thinking and problem-solving skills, and to acquire important knowledge of subject matter. Keywords—Critical Thinking, colloid, Problem Based Learning.
Comparative Study of Searchable Encryption Schemes over Cloud Servers: A Survey

Storing data over cloud servers is very common. However, security issue is a major concern. Data uploaded by owner of data over cloud severs should be secure. To secure data, owner of data first encrypt their data and then upload it on cloud servers. This is the way how data are secure on cloud. But again, security problem arises when we talk about the searching of data over cloud servers. The keywords used for searching over cloud in the form of plain text makes vulnerable. This is another security concern. There are various encrypted searching schemes have been derived. In this paper, we have defined some of them. Specifically attribute based keyword searching scheme and privacy preserving searching schemes are defined. After then we have concluded which is good in performance and security as well.
Simulation Analysis of Spacecraft Automatic Levelling and Equalizing Hoist Device based on Hanging Point Adjustment

In this paper, the virtual prototype of the Spacecraft Automatic Levelling and Equalizing Hoist Device based on Hanging Point Adjustment is modelled and the Automatic levelling and equalizing process is simulated. Variable structure adaptive control system is verified to be asymptotically stable under the interference of the support vehicle. Finally, through the refinement of the ductility of the sling, which is proved that the ductility of the sling has no effect on the stability, the system is proved to have good robustness.
Impact of second-line drug resistance on tuberculosis treatment outcomes in the United States: MDR-TB is bad enough.

SETTING The worldwide emergence of extensively drug-resistant tuberculosis (TB) has focused attention on treatment with second-line drugs (SLDs). OBJECTIVE To determine the impact on outcomes of resistance to individual SLDs, we analyzed successful treatment completion and death among drug-resistant TB cases in the US national TB surveillance system, 1993-2007 (N = 195 518). DESIGN We defined four combinations of first-line drug (FLD) resistance based on isoniazid (INH) and rifamycin, and three patterns of SLD resistance: fluoroquinolones, injectable SLDs and other oral SLDs. We compared treatment outcomes of cases by FLD resistance, with and without each pattern of SLD resistance. RESULTS In all but one instance, cases with FLD resistance but no SLD resistance had better outcomes than cases with SLD resistance. Rifamycin resistance, alone or with INH, resulted in a greater decline in treatment completion and greater increase in deaths than resistance to SLDs. Among patients with multidrug-resistant TB, additional resistance to injectable SLDs was statistically significant. Outcomes were better for human immunodeficiency virus (HIV) negative than HIV-positive cases for all resistance patterns, but improved among HIV-infected cases after 1998, when highly active antiretroviral treatment became widely available. CONCLUSION These results suggest that the effect of rifamycin resistance may outweigh the more modest effects of resistance to specific SLDs.
Using Well-Understood Single-Objective Functions in Multiobjective Black-Box Optimization Test Suites

Abstract Several test function suites are being used for numerical benchmarking of multiobjective optimization algorithms. While they have some desirable properties, such as well-understood Pareto sets and Pareto fronts of various shapes, most of the currently used functions possess characteristics that are arguably underrepresented in real-world problems such as separability, optima located exactly at the boundary constraints, and the existence of variables that solely control the distance between a solution and the Pareto front. Via the alternative construction of combining existing single-objective problems from the literature, we describe the bbob-biobj test suite with 55 bi-objective functions in continuous domain, and its extended version with 92 bi-objective functions (bbob-biobj-ext). Both test suites have been implemented in the COCO platform for black-box optimization benchmarking and various visualizations of the test functions are shown to reveal their properties. Besides providing details on the construction of these problems and presenting their (known) properties, this article also aims at giving the rationale behind our approach in terms of groups of functions with similar properties, objective space normalization, and problem instances. The latter allows us to easily compare the performance of deterministic and stochastic solvers, which is an often overlooked issue in benchmarking.
Data filtering framework for preserving meaningful data records from streams of unstructured weather data

The aim of this research is to design and implement a data filtering framework for preserving meaningful data records from streams of unstructured weather data based on data collection, data pre-process, data filtering, classification, and visualization. The data collection involved monitoring data and data structuring; data pre-process, error checking, error validation, and correction of error; data filtering involved filtering concept, sequential process, and particles filtering while classification data involved the proposed 5M method. The environmental data creates challenges in the context of storage and data processing, which producing large volume of data collection including a massive 30% of unwanted data. It affects the correctness of data in term of usage The rationale and importance of this research comes in forms of creating step by step operations of data filtering method using local weather data that combines data preprocessing and data filtering steps to enhance the accuracy of data classifications. This research successfully reformatted data collected from sensor-boards which are unstructured, with features that structure raw data to a standardized data format. The analysis of the proposed framework employed three measurements approaches for the validation purposes. First, data pre-process component measurement using correctness and precision measures; filtering components are measured by data (indexing and item) using the context of records to discover the duplicates between two different datasets, and finally the 5M classification is measured by the percentage of total and meaningful_ sensitivity to indicated classification of relevant items which explore the meaningful data of measurements based on percentages of classifier data from relevant data. Result shows the pre-processed data collected is reduced by 69.23 % with similar accuracy as compared to the raw data. Classification hit is 84.6 % accuracy to each clustered data. This research has been able to classify streaming weather data. The execution of data filtering framework for preserving meaningful data records from streams of unstructured weather data produced highly accurate classification clusters.
Database Programming under Labwindows/CVI Platform

LabWindows/CVI is one of the most commonly used automatic test system software development platforms. When developing automated test systems, a large amount of test data is often generated. How to implement effective test of a large amount of test data is a problem that is often encountered. In view of this, first introduced the relevant theory and background knowledge of database technology; then discussed how to use ODBC driver and SQL ToolKit software toolkit to operate Access database under CVI platform; Finally, a small program was taken as an example to introduce The specific code of the basic operation of the database is written under the CVI platform. The article provides a reference for the programming of the database under the CVI platform in the automatic test system.
Plant Disease Prediction using Machine Learning Algorithms

Machine learning is the one of the branch in Artificial Intelligence to work automatically or give the instructions to a particular system to perform a action. The goal of machine Learning is to understand the structure of the data and fit that data into models that can be understood and utilized by the people. The proposed research work is for analysis of various machine algorithms applying on plant disease prediction. A plant shows some visible effects of disease, as a response to the pathogen. The visible features such as shape, size, dryness, wilting, are very helpful to recognize the plant condition. The research paper deals with all such features and apply various machine learning technologies to find out the output. The research work deals with decision tree, Naive Bayes theorem, artificial neural network and k-mean clustering and random forest algorithms. Disease development depends on three conditions-host plants susceptible to disease, favorable environment and viable pathogen. The presence of all three conditions is must for a disease to occur.
Wearable technology and health: A bibliometric analysis using SciMAT

Background: To begin research with cross-thematic topics, an analysis is necessary that collects data and historical accounts about these topics. In the field of health and technology, this principle is not unknown. In this case, it is the interest of this investigation to inquire about the topics "wearable" and "health" in order to gather enough information to establish how it has been developed, what the current topics are and what areas it is planned to study in a near future. The purpose of this study is to analyse the growth of the wearable technology and health subject matter. This will create an account of the most relevant themes over time in this subject and their evolution. This study will have an emphasis on recent years, in order to see which themes may be important for future research and publications. Methods: This paper aims at making a bibliometric exploration using information from the Scopus database using the search chain “weareable AND health” within the years of 2000 to 2017. The bibliometric software SciMAT was used for data visualization and analysis. Results: The results obtained are an analysis of the growth rate that is presented in Scopus publications between 2000 and 2017. In addition, a bibliometric analysis is obtained through the SciMat software, which shows the relevant issues that arise through time and that may be relevant for future research. Conclusions: Publications associated with the search chain “Wearable AND Health” from 2000 to 2017 were generally in constant growth over the years. With the SciMAT software, the main theme that was found was that of monitoring systems. In addition, it shows how new themes that are highly relevant emerge, such as mobile technologies and the study of algorithms, which emerge as an evolution from signal processing.
Application of wavelet packet transform in roller bearing fault detection and life estimation

Rolling element bearings are crucial parts of many machines and there has been an increasing demand to find effective and reliable health monitoring technique and advanced signal processing to detect and diagnose the size and location of incipient defects. Condition monitoring of rolling element bearings, comprises four main stages which are, statistical analysis, fault diagnostics, defect size calculation, and prognostics. A novel signal processing algorithm is designed to diagnose localized defects on rolling element bearings components under different operating speeds, loadings, and defect sizes. The algorithm is based on optimizing the ratio of Kurtosis and Shannon entropy to obtain the optimal band pass filter utilizing wavelet packet transform and envelope detection. To experimentally measure the defect size on rolling element bearings using acoustic emission technique, the proposed method along with spectrum of squared Hilbert transform are performed under different rotating speeds, loading conditions, and defect sizes to measure the time difference between the double acoustic emission impulses. Measurement results show the power of the proposed method for experimentally measuring size of different fault shapes using acoustic emission signals. Fatigue life estimation of rolling element bearing has also been investigated utilizing defect size measurements combined with Recursive Least Square Estimation method which is an adaptive algorithm. Experimental results show the effectiveness of recursive least square algorithm for predicting the future defect size on the outer race.
An Automatic Annotation Algorithm for Deep Learning Image Datasets Based on HOG Features

Labeled data is crucial for deep learning model training, but it is mainly based on manual annotation. This paper proposes an automatic annotation algorithm for deep learning image datasets based on HOG features. The algorithm obtains the position information of the target based on the Gaussian mixture model, and automatically obtains the target category based on the Mean Shift algorithm of HOG feature. The labeled sample obtained in the above steps is used to train the Deformable Part Model to annotate more unlabeled image data. Experimental results show that this algorithm can quickly complete the automatic annotation of image datasets, reduce the cost of acquiring labeled datasets and greatly improve the effectiveness of acquiring labeled datasets in deep learning.
PALB2 and RAP80 genes in hereditary breast cancer predisposition

Around 5–10% of all breast cancers stem from a strong hereditary predisposition to the disease. Mutations in currently known breast cancer predisposing genes, however, account for only 25–30% of all hereditary breast cancer cases, BRCA1 and BRCA2 being the two major ones. Since BRCA1 and BRCA2 participate in the DNA damage response, mutations in other genes, such as PALB2 and RAP80, which are involved in these pathways, may also predispose to breast tumours. Therefore, the aim of this study was to evaluate variations of the human PALB2 and RAP80 genes as novel potential candidates for breast cancer susceptibility and to further characterize the role of PALB2-deficiency in cancer development. A mutation, c.1592delT, was identified in PALB2 at an elevated frequency among breast cancer patients (0.9%) compared to controls (0.2%) (P = 0.003, OR 3.94, 95% CI 1.5–12.1). Among familial cases the frequency was even higher (2.7%). This mutation represents a genuine loss-of-function mutation since its protein product showed significantly decreased BRCA2binding affinity and could neither support homologous recombination nor restore crosslink repair in PALB2-deficient cells. Heterozygous PALB2 c.1592delT carriers displayed haploinsufficiency of PALB2 marked by aberrant DNA replication and DNA damage response that led to a significant increase in genomic instability. As the tumours were negative for loss of heterozygosity at this chromosomal locus, these findings provide a mechanism for the early stages of breast cancer development in PALB2 c.1592delT carriers. Palb2 was also found to play a critical role in early mouse development. As in Brca1/2deficient embryos, homozygous inactivation of Palb2 resulted in embryonic lethality due to mesoderm differentiation and cell proliferation defects. The phenotypic similarity of Palb2 and Brca1/2-deficient mice further supports the close functional relationship shown in vitro for these proteins. A novel mutation, delE81, was identified in RAP80 in one out of 112 breast cancer families, and in one patient diagnosed with bilateral breast cancer out of 503 unselected breast cancers. The resultant delE81 protein displayed significantly reduced ubiquitin binding and double-strand break (DSB) localization. Furthermore, it impaired the recruitment of the whole BRCA1-A complex to the site of DSBs, thus compromising BRCA1-mediated DNA damage response signalling. Although the mutation is quite rare, the current results indicate that the RAP80 delE81 defect is biologically relevant and is likely associated with a hereditary predisposition to breast cancer.
Student Engagement in Nursing School: A Secondary Analysis of the National Survey of Student Engagement Data

Student engagement has received considerable attention in higher education research because of the link between increased student knowledge, greater student satisfaction with educational experience, and increased student retention and persistence. The National Survey of Student Engagement (NSSE) has been used since 2000 to assess engagement in undergraduate college students. NSSE results have been used to gain an understanding about levels of academic engagement for freshman and senior college students. Institutions use NSSE results to make changes in policies and practices to improve undergraduate education. This comparative descriptive study examined levels of undergraduate nursing students’ engagement during college by conducting a secondary analysis of NSSE data. The overall aim of this study was to gain a better understanding of nursing students’ levels of engagement at two points in time and comparing two geographic regions, and how they spent their time while in college. In a 2007 report, the National Leadership Council for Liberal Education and America’s Promise (LEAP) identified ten innovative high-impact practices in higher education. Since then, these practices have been implemented across the nation and have been associated with gains in student learning and personal development (Kuh, 2008). This study compared senior nursing students’ levels of engagement before and after these high-impact practices were recommended to see if engagement levels in senior nursing students differed between 2003 and 2010. Astin’s student involvement theory was used as a guiding framework for this study to examine how nursing students engage in the learning process and what educational resources nursing students use to become involved in the learning process. Astin’s theory focuses on what the college student does to be an active participant in the learning process and describes the environmental influences on college student development.