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1

Qin, Xiangju, Yang Zhang, Chen Li, and Xue Li. "Learning from data streams with only positive and unlabeled data." Journal of Intelligent Information Systems 40, no. 3 (January 5, 2013): 405–30. http://dx.doi.org/10.1007/s10844-012-0231-6.

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Terada, Yoshikazu, Issei Ogasawara, and Ken Nakata. "Classification from only positive and unlabeled functional data." Annals of Applied Statistics 14, no. 4 (December 2020): 1724–42. http://dx.doi.org/10.1214/20-aoas1404.

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3

Becerra-Bonache, Leonor. "Learning SECp Languages from Only Positive Data." Triangle, no. 8 (June 29, 2018): 1. http://dx.doi.org/10.17345/triangle8.1-18.

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The eld of Grammatical Inference provides a good theoretical framework for investigating a learning process. Formal results in this eld can be relevant to the question of rst language acquisition. However, Grammatical Inference studies have been focused mainly on mathematical aspects, and have not exploited the linguistic relevance of their results. With this paper, we try to enrich Grammatical Inference studies with ideas from Linguistics. We propose a non-classical mechanism that has relevant linguistic and computational properties, and we study its learnability from positive data.
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Daneshpazhouh, Armin, and Ashkan Sami. "Semi-Supervised Outlier Detection with Only Positive and Unlabeled Data Based on Fuzzy Clustering." International Journal on Artificial Intelligence Tools 24, no. 03 (June 2015): 1550003. http://dx.doi.org/10.1142/s0218213015500037.

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The task of semi-supervised outlier detection is to find the instances that are exceptional from other data, using some labeled examples. In many applications such as fraud detection and intrusion detection, this issue becomes more important. Most existing techniques are unsupervised. On the other hand, semi-supervised approaches use both negative and positive instances to detect outliers. However, in many real world applications, very few positive labeled examples are available. This paper proposes an innovative approach to address this problem. The proposed method works as follows. First, so
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5

Tomeo, Paolo, Ignacio Fernández-Tobías, Iván Cantador, and Tommaso Di Noia. "Addressing the Cold Start with Positive-Only Feedback Through Semantic-Based Recommendations." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, Suppl. 2 (December 2017): 57–78. http://dx.doi.org/10.1142/s0218488517400116.

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Recommender systems aim to provide users with accurate item suggestions in a personalized fashion, but struggle in the case of cold start users, for whom there is a scarcity of preference data. User preferences can be either explicitly stated by the users — often by means of ratings —, or implicitly acquired by a system — for instance by mining text reviews, search queries, and purchase records. Recommendation methods have been mostly designed to deal with numerical ratings. However, real scenarios with user preferences expressed in the form of binary and unary (positive-only) feedback, e.g. t
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Leibowitz, Arleen A., and Katherine Desmond. "Do Only 21% of HIV-Positive Medicaid Enrollees Link to Treatment? Challenges in Interpreting Medicaid Claims Data." Sexually Transmitted Diseases 40, no. 7 (July 2013): 582. http://dx.doi.org/10.1097/01.olq.0000430802.91969.98.

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7

Cheng, Zhanzhan, Shuigeng Zhou, and Jihong Guan. "Computationally predicting protein-RNA interactions using only positive and unlabeled examples." Journal of Bioinformatics and Computational Biology 13, no. 03 (May 15, 2015): 1541005. http://dx.doi.org/10.1142/s021972001541005x.

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Protein–RNA interactions (PRIs) are considerably important in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulations of gene expression to the active defense of host against virus. With the development of high throughput technology, large amounts of PRI information is available for computationally predicting unknown PRIs. In recent years, a number of computational methods for predicting PRIs have been developed in the literature, which usually artificially construct negative samples based on verified nonredundant datasets of PRIs to train classi
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Kamaludin, Hazalila, Hairulnizam Mahdin, and Jemal H. Abawajy. "Filtering Redundant Data from RFID Data Streams." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7107914.

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Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we prop
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9

KOBAYASHI, SATOSHI, and TAKASHI YOKOMORI. "FAMILIES OF NONCOUNTING LANGUAGES AND THEIR LEARNABILITY FROM POSITIVE DATA." International Journal of Foundations of Computer Science 07, no. 04 (December 1996): 309–27. http://dx.doi.org/10.1142/s0129054196000221.

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This paper introduces some subclasses of noncounting languages and presents some results on the learnability of the classes from positive data. We first establish several relationships among the language classes introduced and the class of reversible languages. Especially, we introduce the notion of local parsability, and define a class (k, l)-CLTS, which is a subclass of the class of concatenations of strictly locally testable languages. We show its close relation to the class of reversible languages. We then study on the relationship between the closure of the Boolean operations and the lear
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10

Sakai, Tomoya, and Nobuyuki Shimizu. "Covariate Shift Adaptation on Learning from Positive and Unlabeled Data." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4838–45. http://dx.doi.org/10.1609/aaai.v33i01.33014838.

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The goal of binary classification is to identify whether an input sample belongs to positive or negative classes. Usually, supervised learning is applied to obtain a classification rule, but in real-world applications, it is conceivable that only positive and unlabeled data are accessible for learning, which is called learning from positive and unlabeled data (PU learning). Furthermore, in practice, data distributions are likely to differ between training and testing due to, for example, time variation and domain shift. The covariate shift is a dataset shift situation, where distributions of c
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11

Anderson, Joseph C., Paul Limburg, Christina Robinson, William Hisey, and Lynn Butterly. "ID: 3517867 COMPARISON OF COLONOSCOPY FINDINGS IN PATIENTS WITH PRECEDING FIT POSITIVE AND MT-SDNA POSITIVE TESTS TO PATIENTS HAVING A COLONOSCOPY ONLY: DATA FROM THE NEW HAMPSHIRE COLONOSCOPY REGISTRY." Gastrointestinal Endoscopy 93, no. 6 (June 2021): AB34—AB35. http://dx.doi.org/10.1016/j.gie.2021.03.133.

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LONGACRE, ANGELIKA, L. RIDGWAY SCOTT, and JERROLD S. LEVINE. "LINEAR INDEPENDENCE OF PAIRWISE COMPARISONS OF DNA MICROARRAY DATA." Journal of Bioinformatics and Computational Biology 03, no. 06 (December 2005): 1243–62. http://dx.doi.org/10.1142/s0219720005001600.

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Motivation: For DNA microarrays, the gain in certainty by performing multiple experimental repeats is offset by the high cost of each experiment. In a typical experiment, two independent measurements (that is, data from two separate arrays) are combined to yield a single comparative index per gene. Thus, although one uses 2n arrays and performs 2n independent measurements, one obtains only n comparative measurements. We addressed the question: how many of the potential n2 comparisons derivable from such data are actually independent, and what effect do these additional comparisons have on the
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13

Stock, Eileen M., James D. Stamey, Rengaswamy Sankaranarayanan, Dean M. Young, Richard Muwonge, and Marc Arbyn. "Estimation of disease prevalence, true positive rate, and false positive rate of two screening tests when disease verification is applied on only screen-positives: A hierarchical model using multi-center data." Cancer Epidemiology 36, no. 2 (April 2012): 153–60. http://dx.doi.org/10.1016/j.canep.2011.07.001.

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14

Teoh, Siew Hong, and Yinglei Zhang. "Data Truncation Bias, Loss Firms, and Accounting Anomalies." Accounting Review 86, no. 4 (April 1, 2011): 1445–75. http://dx.doi.org/10.2308/accr-10032.

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ABSTRACT Ex post trimming of extreme returns observations that are not data errors causes spurious inferences in tests of market efficiency and behavioral explanations for anomalies. Trimming causes a downward truncation bias in estimated mean returns that is stronger in ex ante subsamples with more loss firms and in which return distributions are more right-skewed. There is an asymmetric U-shaped relation between return right-skewness and loss frequency across deciles of negative return predictors (Accruals, ΔNOA, and NOA), and a downward sloping relationship for positive return predictors (C
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15

Ghiyasi, Mojtaba, and Ning Zhu. "An inverse semi-oriented radial data envelopment analysis measure for dealing with negative data." IMA Journal of Management Mathematics 31, no. 4 (April 29, 2020): 505–16. http://dx.doi.org/10.1093/imaman/dpaa007.

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Abstract The conventional inverse data envelopment analysis (DEA) model is only applicable to positive data, while negative data are commonly present in most real-world applications. This paper proposes a novel inverse DEA model that can handle negative data. The conventional inverse DEA model is a special case of our model as our model is more general in terms of returns-to-scale properties. The proposed model is used to evaluate the efficiency of the Chinese commercial banks after the global financial crisis, where negative outputs existed. We show that our model is feasible in the presence
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16

Jiang, Xiaoqi, Tiantian Xu, and Xiangjun Dong. "Campus Data Analysis Based on Positive and Negative Sequential Patterns." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (April 8, 2019): 1959016. http://dx.doi.org/10.1142/s021800141959016x.

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Campus data analysis is becoming increasingly important in mining students’ behavior. The consumption data of college students is an important part of the campus data, which can reflect the students’ behavior to a great degree. A few methods have been used to analyze students’ consumption data, such as classification, association rules, clustering, decision trees, time series, etc. However, they do not use the method of sequential patterns mining, which results in some important information missing. Moreover, they only consider the occurring (positive) events but do not consider the nonoccurri
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Lorey, Fred. "Human Genetics Data Applied to Genetic Screening Programs." Practicing Anthropology 20, no. 2 (April 1, 1998): 30–33. http://dx.doi.org/10.17730/praa.20.2.n84728r821185380.

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The uses of human genetic data in genetic screening are multifaceted and dynamic, creating an ongoing stream of useful prevalence data, ethnicity data, and natural history information. Since the primary facility for generation of these data is a large public health genetic screening program, however, the results must be continually analyzed and evaluated in the context of testing parameters. For example, presumptive positive rates (initial screening test positives, only a portion of which will become diagnosed cases), false positive rates, detection rates, and analytical values must be constan
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18

Gawne, Lauren, Chelsea Krajcik, Helene N. Andreassen, Andrea L. Berez-Kroeker, and Barbara F. Kelly. "Data transparency and citation in the journal Gesture." Gesture 18, no. 1 (December 31, 2019): 83–109. http://dx.doi.org/10.1075/gest.00034.gaw.

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Abstract Data is central to scholarly research, but the nature and location of data used is often under-reported in research publications. Greater transparency and citation of data have positive effects for the culture of research. This article presents the results of a survey of data citation in six years of articles published in the journal Gesture (12.1–17.2). Gesture researchers draw on a broad range of data types, but the source and location of data are often not disclosed in publications. There is also still a strong research focus on only a small range of the world’s languages and their
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19

Fang*, Wei, Yang Zheng*, Fan Yang, Meng-Ting Cai, Chun-Hong Shen, Zhi-Rong Liu, Yin-Xi Zhang, and Mei-Ping Ding. "Short segment myelitis as the initial and only manifestation of aquaporin-4 immunoglobulin G-positive neuromyelitis optica spectrum disorders." Therapeutic Advances in Neurological Disorders 13 (January 2020): 175628641989859. http://dx.doi.org/10.1177/1756286419898594.

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Background: Short segment myelitis (SSM, < 3 vertebral segments) is an under-recognized initial manifestation of neuromyelitis optica spectrum disorders (NMOSD). Though infrequent, failure to recognize SSM in patients with NMOSD would lead to incorrect diagnosis and treatment. Therefore, delineation of features of NMOSD-associated SSM is of paramount importance. Objective: Our study aimed to determine the demographic, clinical and radiological features of NMOSD-associated SSM, and compare those with NMOSD-associated longitudinally extensive transverse myelitis (LETM) and multiple sclerosis
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Wang, Siye, Ziwen Cao, Yanfang Zhang, Weiqing Huang, and Jianguo Jiang. "A Temporal and Spatial Data Redundancy Processing Algorithm for RFID Surveillance Data." Wireless Communications and Mobile Computing 2020 (February 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/6937912.

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The Radio Frequency Identification (RFID) data acquisition rate used for monitoring is so high that the RFID data stream contains a large amount of redundant data, which increases the system overhead. To balance the accuracy and real-time performance of monitoring, it is necessary to filter out redundant RFID data. We propose an algorithm called Time-Distance Bloom Filter (TDBF) that takes into account the read time and read distance of RFID tags, which greatly reduces data redundancy. In addition, we have proposed a measurement of the filter performance evaluation indicators. In experiments,
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21

Shield, Jennifer, Sabine Braat, Matthew Watts, Gemma Robertson, Miles Beaman, James McLeod, Robert W. Baird, et al. "Seropositivity and geographical distribution of Strongyloides stercoralis in Australia: A study of pathology laboratory data from 2012–2016." PLOS Neglected Tropical Diseases 15, no. 3 (March 9, 2021): e0009160. http://dx.doi.org/10.1371/journal.pntd.0009160.

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Background There are no national prevalence studies of Strongyloides stercoralis infection in Australia, although it is known to be endemic in northern Australia and is reported in high risk groups such as immigrants and returned travellers. We aimed to determine the seropositivity (number positive per 100,000 of population and percent positive of those tested) and geographical distribution of S. stercoralis by using data from pathology laboratories. Methodology We contacted all seven Australian laboratories that undertake Strongyloides serological (ELISA antibody) testing to request de-identi
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22

Zhao, Xiangyu, Zhendong Niu, Kaiyi Wang, Ke Niu, and Zhongqiang Liu. "Improving Top-NRecommendation Performance Using Missing Data." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/380472.

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Recommender systems become increasingly significant in solving the information explosion problem. Data sparse is a main challenge in this area. Massive unrated items constitute missing data with only a few observed ratings. Most studies consider missing data as unknown information and only use observed data to learn models and generate recommendations. However, data are missing not at random. Part of missing data is due to the fact that users choose not to rate them. This part of missing data is negative examples of user preferences. Utilizing this information is expected to leverage the perfo
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23

Duan, Xiao Gang, and Feng Gao. "Application of Data Mining on Stock Market." Applied Mechanics and Materials 513-517 (February 2014): 1352–55. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1352.

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Forecasting the stock market price movements is now popular in the field of financial research. A large number of scholars has carried on the positive exploration. Only these people are more focused on selection of prediction methods and algorithm optimization. In view of the stock market time series has the nature of the multi-scale features, nonstationary and nonlinear properties and low signal-to-noise ratio of some different from other general characteristics of time series, this paper puts forward building a multi-scale technique index method for preprocessing of the input data and then u
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Ryabko, Boris. "Time-Universal Data Compression." Algorithms 12, no. 6 (May 29, 2019): 116. http://dx.doi.org/10.3390/a12060116.

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Nowadays, a variety of data-compressors (or archivers) is available, each of which has its merits, and it is impossible to single out the best ones. Thus, one faces the problem of choosing the best method to compress a given file, and this problem is more important the larger is the file. It seems natural to try all the compressors and then choose the one that gives the shortest compressed file, then transfer (or store) the index number of the best compressor (it requires log m bits, if m is the number of compressors available) and the compressed file. The only problem is the time, which essen
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Vilar, Polona, and Vlasta Zabukovec. "Research data management and research data literacy in Slovenian science." Journal of Documentation 75, no. 1 (January 14, 2019): 24–43. http://dx.doi.org/10.1108/jd-03-2018-0042.

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PurposeThe purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level.Design/methodology/approachPurposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate me
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Polak, Jonathan, Ogheneruona Odili, Mary Ashleigh Craver, Anthony Mayen, Kyle Purrman, Asem Rahman, Charlie Joseph Sang, and Paul P. Cook. "Clostridium difficile Testing Algorithm: Is There a Difference in Patients Who Test Positive by Enzyme Immunoassay vs. Those Who Only Test Positive by Nucleic Acid Amplification Methodology?" Open Forum Infectious Diseases 4, suppl_1 (2017): S394. http://dx.doi.org/10.1093/ofid/ofx163.981.

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Abstract Background Testing for Clostridium difficile infection (CDI) commonly involves checking for the presence of toxins A and B by enzyme immunoassay (EIA) or nucleic acid amplification (NAA). The former is very specific, but not very sensitive. The latter is very sensitive. Beginning in 2011, our hospital incorporated an algorithm that involved testing liquid stool specimens for glutamate dehydrogenase (GDH) and toxin by EIA. For discrepant results, the stool specimen was tested for the presence of toxin by NAA. We sought to determine whether there was a difference in the baseline charact
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Tontini, Fabio Caratori, Osvaldo Faggioni, Nicolò Beverini, and Cosmo Carmisciano. "Gaussian envelope for 3D geomagnetic data inversion." GEOPHYSICS 68, no. 3 (May 2003): 996–1007. http://dx.doi.org/10.1190/1.1581071.

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We describe an inversion method for 3D geomagnetic data based on approximation of the source distribution by means of positive constrained Gaussian functions. In this way, smoothness and positivity are automatically imposed on the source without any subjective input from the user apart from selecting the number of functions to use. The algorithm has been tested with synthetic data in order to resolve sources at very different depths, using data from one measurement plane only. The forward modeling is based on prismatic cell parameterization, but the algebraic nonuniqueness is reduced because a
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KALININ, BORIS, and VICTORIA SADOVSKAYA. "On Anosov diffeomorphisms with asymptotically conformal periodic data." Ergodic Theory and Dynamical Systems 29, no. 1 (February 2009): 117–36. http://dx.doi.org/10.1017/s0143385708000357.

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AbstractWe consider transitive Anosov diffeomorphisms for which every periodic orbit has only one positive and one negative Lyapunov exponent. We prove various properties of such systems, including strong pinching, C1+β smoothness of the Anosov splitting, and C1 smoothness of measurable invariant conformal structures and distributions. We apply these results to volume-preserving diffeomorphisms with two-dimensional stable and unstable distributions and diagonalizable derivatives of the return maps at periodic points. We show that a finite cover of such a diffeomorphism is smoothly conjugate to
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Jaskie, Kristen, Joshua Martin, and Andreas Spanias. "PV Fault Detection Using Positive Unlabeled Learning." Applied Sciences 11, no. 12 (June 17, 2021): 5599. http://dx.doi.org/10.3390/app11125599.

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Solar array management and photovoltaic (PV) fault detection is critical for optimal and robust performance of solar plants. PV faults cause substantial power reduction along with health and fire hazards. Traditional machine learning solutions require large, labeled datasets which are often expensive and/or difficult to obtain. This data can be location and sensor specific, noisy, and resource intensive. In this paper, we develop and demonstrate new semi supervised solutions for PV fault detection. More specifically, we demonstrate that a little-known area of semi-supervised machine learning c
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Kaari, Jennifer. "Researchers at Arab Universities Hold Positive Views on Research Data Management and Data Sharing." Evidence Based Library and Information Practice 15, no. 2 (June 15, 2020): 168–70. http://dx.doi.org/10.18438/eblip29746.

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A Review of:
 Elsayed, A. M., & Saleh, E. I. (2018). Research data management and sharing among researchers in Arab universities: An exploratory study. IFLA Journal, 44(4), 281–299. https://doi.org/10.1177/0340035218785196
 Abstract
 Objective – To investigate researchers’ practices and attitudes regarding research data management and data sharing.
 Design – Email survey.
 Setting – Universities in Egypt, Jordan, and Saudi Arabia.
 Subjects – Surveys were sent to 4,086 academic faculty researchers.
 Methods – The survey was emailed to faculty at three Ara
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Ahmad, Ijaz, De Shan Tang, Mei Wang, and Sarfraz Hashim. "Trend Analysis on Precipitation Time Series Data in Munda Catchment, Pakistan." Applied Mechanics and Materials 692 (November 2014): 97–102. http://dx.doi.org/10.4028/www.scientific.net/amm.692.97.

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This paper investigates the trends in precipitation time series of 10 stations for the time period of 51 years (1961-2011) in the Munda catchment, Pakistan. The Mann-Kendall (MK) and Spearman’s rho (SR) tests were employed for detection of the trend on the seasonal and annual basis at 5% significance level. For the removal of the serial correlation Trend Free Pre-Whitening approach was applied. The results show, a mixture of positive (increasing) and negative (decreasing) trends. A shift in precipitation time series is observed on seasonal scale from summer to autumn season. The Charbagh stati
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Quinquenel, Anne, Chadi Al Nawakil, Fanny Baran-Marszak, Virginie Eclache, Remi Letestu, Christine Le Roy, Nadine Varin-Blank, Alain Delmer, Vincent Levy, and Florence Cymbalista. "Old DAT / New Data : Positive Direct Antiglobulin Test Identifies a Specific Subgroup Of CLL Patients." Blood 122, no. 21 (November 15, 2013): 4154. http://dx.doi.org/10.1182/blood.v122.21.4154.4154.

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Abstract Autoimmune cytopenias represent a well-known complication of chronic lymphocytic leukemia (CLL). The most frequent one is autoimmune haemolytic anemia (AIHA), occurring in about 5% of the patients during the course of the disease. The prognostic significance of autoimmune cytopenias remains uncertain and controversial. In previous studies, a positive direct antiglobulin test (DAT) was found in up to 35% of CLL cases, however only a minority of the patients would develop AIHA at any time of the disease. The aim of this monocentric study was to explore the clinical and biological charac
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Lam, Son K., Stefan Sleep, Thorsten Hennig-Thurau, Shrihari Sridhar, and Alok R. Saboo. "Leveraging Frontline Employees’ Small Data and Firm-Level Big Data in Frontline Management." Journal of Service Research 20, no. 1 (November 18, 2016): 12–28. http://dx.doi.org/10.1177/1094670516679271.

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The advent of new forms of data, modern technology, and advanced data analytics offer service providers both opportunities and risks. This article builds on the phenomenon of big data and offers an integrative conceptual framework that captures not only the benefits but also the costs of big data for managing the frontline employee (FLE)-customer interaction. Along the positive path, the framework explains how the “3Vs” of big data (volume, velocity, and variety) have the potential to improve service quality and reduce service costs by influencing big data value and organizational change at th
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Shofiya, Carol, and Samina Abidi. "Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data." International Journal of Environmental Research and Public Health 18, no. 11 (June 3, 2021): 5993. http://dx.doi.org/10.3390/ijerph18115993.

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Background: COVID-19 preventive measures have been an obstacle to millions of people around the world, influencing not only their normal day-to-day activities but also affecting their mental health. Social distancing is one such preventive measure. People express their opinions freely through social media platforms like Twitter, which can be shared among other users. The articulated texts from Twitter can be analyzed to find the sentiments of the public concerning social distancing. Objective: To understand and analyze public sentiments towards social distancing as articulated in Twitter textu
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Kabir Ahmad, Farzana, and Siti Sakira Kamaruddin. "Research Trends in Microarray Data Analysis: Modelling Gene Regulatory Network by Integrating Transcription Factors Data." Scientific Research Journal 12, no. 1 (June 1, 2015): 39. http://dx.doi.org/10.24191/srj.v12i1.5437.

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The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the genetic regulatory processes would bring significant implications in the biomedical fields and many other pharmaceutical industries. As a result, various mathematical and computational methods have been used to model gene regulatory network from microa
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Cui, Zhenghang, Nontawat Charoenphakdee, Issei Sato, and Masashi Sugiyama. "Classification from Triplet Comparison Data." Neural Computation 32, no. 3 (March 2020): 659–81. http://dx.doi.org/10.1162/neco_a_01262.

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Abstract (sommario):
Learning from triplet comparison data has been extensively studied in the context of metric learning, where we want to learn a distance metric between two instances, and ordinal embedding, where we want to learn an embedding in a Euclidean space of the given instances that preserve the comparison order as much as possible. Unlike fully labeled data, triplet comparison data can be collected in a more accurate and human-friendly way. Although learning from triplet comparison data has been considered in many applications, an important fundamental question of whether we can learn a classifier only
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37

Prester, Jasna, and Mihaela Jurić. "Big data for product innovation in manufacturing." Tehnički glasnik 13, no. 1 (March 23, 2019): 36–42. http://dx.doi.org/10.31803/tg-20181011124610.

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Abstract (sommario):
The article analyses big data usage in the Croatian manufacturing sector. Big data usage is still low but present. We analysed the influence of six sources of big data and their influence on share of returns generated by new products using two step OLS regression analysis. The results are robust but they show that some sources have positive and some have negative effects on share of returns generated by new products. Based on the most recent research of scholarly papers we define big data and show a clear research gap by linking big data and innovation. That is, only six papers deal with big d
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38

Dagogo-Jack, Ibiayi, Geoffrey R. Oxnard, Jessica Fink, Gianluca Diubaldi, Caitlyn Helms, Justin F. Gainor, Michael S. Rabin, et al. "A phase II study of lorlatinib in patients (pts) with ALK-positive (ALK+) lung cancer with brain-only progression." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): 9595. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.9595.

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Abstract (sommario):
9595 Background: Lorlatinib is a 3rd-generation ALK tyrosine kinase inhibitor (TKI) developed to penetrate the central nervous system (CNS) and overcome resistance to 2nd-generation (2nd-gen) ALK TKIs. In a phase II study, lorlatinib demonstrated significant intracranial (IC) activity after failure of 2nd-gen TKIs. As treatment discontinuation for extracranial (EC) progression can confound assessment of durability of IC response, we performed a phase II study (NCT02927340) to selectively evaluate lorlatinib activity in ALK+ pts with CNS-only disease. Methods: Between 11/2016 and 1/2019, 22 pts
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39

Rose, Ashley, Runa Gokhale, Isaac See, James Baggs, Rachel Slayton, Scott Fridkin, and Kelly Hatfield. "Validation of Administrative Codes for Identification of Staphylococcus aureus Infections Among Electronic Health Data." Infection Control & Hospital Epidemiology 41, S1 (October 2020): s507—s509. http://dx.doi.org/10.1017/ice.2020.1188.

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Abstract (sommario):
Background: Epidemiological studies have utilized administrative discharge diagnosis codes to identify methicillin-resistant and methicillin-sensitive Staphylococcus aureus (MRSA and MSSA) infections and trends, despite debate regarding the accuracy of utilizing codes for this purpose. We assessed the sensitivity and positive predictive value (PPV) of MRSA- and MSSA-specific diagnosis codes, trends, characteristics, and outcomes of S. aureus hospitalizations by method of identification. Methods: Clinical micro biology results and discharge data from geographically diverse US hospitals particip
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40

Hawser, Stephen P., Samuel K. Bouchillon, Daryl J. Hoban, Robert E. Badal, Po-Ren Hsueh та David L. Paterson. "Emergence of High Levels of Extended-Spectrum-β-Lactamase-Producing Gram-Negative Bacilli in the Asia-Pacific Region: Data from the Study for Monitoring Antimicrobial Resistance Trends (SMART) Program, 2007". Antimicrobial Agents and Chemotherapy 53, № 8 (8 червня 2009): 3280–84. http://dx.doi.org/10.1128/aac.00426-09.

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Abstract (sommario):
ABSTRACT Of 3,004 gram-negative bacilli collected from intra-abdominal infections in the Asia-Pacific region during 2007, 42.2% and 35.8% of Escherichia coli and Klebsiella spp., respectively, were extended-spectrum β-lactamase (ESBL) positive. Moreover ESBL rates in India for E. coli, Klebsiella pneumoniae, and Klebsiella oxytoca were 79.0%, 69.4%, and 100%, respectively. ESBL-positive E. coli rates were also relatively high in China (55.0%) and Thailand (50.8%). Ertapenem and imipenem were the most active drugs tested, inhibiting over 90% of all species, including ESBL-positive isolates with
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41

Rüber, Ina Elisabeth, and Jan Germen Janmaat. "Does Participation in Adult Education Increase Volunteering? An Analysis of British Longitudinal Data." Adult Education Quarterly 71, no. 1 (June 2, 2020): 55–72. http://dx.doi.org/10.1177/0741713620927348.

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Abstract (sommario):
High-quality longitudinal data from the UK Household Longitudinal Study gives us the opportunity to investigate whether participation in adult education (AE) fosters volunteering, and whether this depends on the volume of AE, its content, or on the qualification obtained with it. From a public enlightenment perspective, we would only expect to find an effect if the content of AE relates to social issues and domains relevant for volunteering. Yet theories emphasizing AE as a place of encounter and an activity that strengthens self-confidence would expect a positive effect regardless of the cont
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42

Viguier, Jérôme, Yvan Coscas, Chantal Touboul, Jean F. Morere, Jean-Yves Blay, Xavier B. Pivot, Christine Lhomel, and François Eisinger. "Knowledge of the French population on colorectal cancer screening: Data from the EDIFICE 3 survey." Journal of Clinical Oncology 31, no. 4_suppl (February 1, 2013): 352. http://dx.doi.org/10.1200/jco.2013.31.4_suppl.352.

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Abstract (sommario):
352 Background: In France, following a pilot population-based screening program in 2002-2003, a national organized program targeting 17 million people was progressively implemented from 2005 to 2009. EDIFICE surveys are iterative polls focusing on cancer screening behavior. The EDIFICE 3 survey was conducted in 2011 and partly dedicated to knowledge of the colorectal cancer screening process. Methods: This third nationwide observational study, EDIFICE 3, was conducted via phone interviews among a representative sample of 946 subjects aged between 50 and 74 years, who had never been treated for
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43

Fan, Yunzhou, Yanyan Wu, Xiongjing Cao, Junning Zou, Ming Zhu, Di Dai, Lin Lu, Xiaoxv Yin, and Lijuan Xiong. "Automated Cluster Detection of Health Care–Associated Infection Based on the Multisource Surveillance of Process Data in the Area Network: Retrospective Study of Algorithm Development and Validation." JMIR Medical Informatics 8, no. 10 (October 23, 2020): e16901. http://dx.doi.org/10.2196/16901.

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Abstract (sommario):
Background The cluster detection of health care–associated infections (HAIs) is crucial for identifying HAI outbreaks in the early stages. Objective We aimed to verify whether multisource surveillance based on the process data in an area network can be effective in detecting HAI clusters. Methods We retrospectively analyzed the incidence of HAIs and 3 indicators of process data relative to infection, namely, antibiotic utilization rate in combination, inspection rate of bacterial specimens, and positive rate of bacterial specimens, from 4 independent high-risk units in a tertiary hospital in C
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44

Tao, Min, Xiaobin Gong, Jian Guan, Junfeng Song, Zhixin Song, Xueyan Li, Shuxu Guo, Jian Chen, Siyao Yu, and Fengli Gao. "Ghost Imaging by a Proportional Parameter to Filter Bucket Data." Applied Sciences 11, no. 1 (December 29, 2020): 227. http://dx.doi.org/10.3390/app11010227.

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Abstract (sommario):
Most ghost imaging reconstruction algorithms require a large measurement time to retrieve the object information clearly. But not all groups of data play a positive role in reconstructing the object image. Abandoning some redundant data can not only enhance the quality of reconstruction images but also speed up the computation process. Here, we propose a method to screen the data using two threshold values set by a proportional parameter during the sampling process. Experimental results show that the reserved data after screening can be used in several reconstruction algorithms, and the recons
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45

Zancan, M., R. Franceschini, C. Mimmo, M. Vianello, F. Di Tonno, C. Mazzariol, G. Malossini, and M. Gion. "Free DNA in Urine: A New Marker for Bladder Cancer? Preliminary Data." International Journal of Biological Markers 20, no. 2 (April 2005): 134–36. http://dx.doi.org/10.1177/172460080502000209.

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Abstract (sommario):
The aim of the present preliminary study was to investigate the presence of free DNA (FDNA) in urine as a possible marker for the diagnosis of bladder cancer. Naturally voided morning urine specimens were collected from 57 patients with suspected bladder cancer before cystoscopy. A standard urine test was performed; the specimens were then processed in order to obtain a quantitative evaluation of the presence of free DNA in the urine. Twenty-two patients were excluded from the study because they had leukocyturia and/or bacteriuria. Free DNA concentrations higher than 250 ng/mL were found in al
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46

Liu, Hai Yan. "Designed Data Mining System Based on the Distribute Intrusion Detection Designed." Advanced Materials Research 1056 (October 2014): 202–5. http://dx.doi.org/10.4028/www.scientific.net/amr.1056.202.

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Abstract (sommario):
With the development of computer and communication technology, the network has become an important part of the global information infrastructure, but the security problem "is constantly exposed to the vast majority of only using firewall this passive defense means within the network, there are still" great security risk which needs some positive active! The defense strategy, the most important one of which is intrusion detection system through the analysis of network or system in a number of key points to collect information and its, find the breach of security strategy behavior and the signs
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47

Hou, Shiying, and Liangrong Song. "Market Integration and Regional Green Total Factor Productivity: Evidence from China’s Province-Level Data." Sustainability 13, no. 2 (January 6, 2021): 472. http://dx.doi.org/10.3390/su13020472.

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Abstract (sommario):
The development of market integration has an important effect on regional green total factor productivity (GTFP). Based on the panel data of 30 provinces in China from 2008 to 2017, this paper studies the spatial effect and transmission mechanism of market integration on regional green total factor productivity by calculating the Malmquist–Luenburger index and using spatial econometric models. It was found that market integration can promote the improvement of regional green total factor productivity. This positive effect is not only directly reflected in the region, it also indirectly promote
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48

Liu, Chanyuan. "Individual investor attention and stock market performance——Based on big data measurement." E3S Web of Conferences 253 (2021): 02009. http://dx.doi.org/10.1051/e3sconf/202125302009.

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Abstract (sommario):
Investor attention is a Sizerce resource, and only the information that investors pay attention to can be reflected in the stock market through transactions. Considering about the development of big data, this paper uses data mining and crawling software to crawl the daily indicators of individual investor attention in 2019. Through a fixed-effect panel data regression model, this paper examines the relationship of personal attention and stock market performance indicators over the same period. This paper found that individual investors’ attention can have a significant positive correlation wi
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49

Zhou, Chen, Mark D. Zelinka, Andrew E. Dessler, and Ping Yang. "An Analysis of the Short-Term Cloud Feedback Using MODIS Data." Journal of Climate 26, no. 13 (July 1, 2013): 4803–15. http://dx.doi.org/10.1175/jcli-d-12-00547.1.

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Abstract (sommario):
Abstract The cloud feedback in response to short-term climate variations is estimated from cloud measurements combined with offline radiative transfer calculations. The cloud measurements are made by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite and cover the period 2000–10. Low clouds provide a strong negative cloud feedback, mainly because of their impact in the shortwave (SW) portion of the spectrum. Midlevel clouds provide a positive net cloud feedback that is a combination of a positive SW feedback partially canceled by a negative feedback in the long
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50

Mishra, Amrita. "A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 2, no. 2 (June 5, 2021): 1–10. http://dx.doi.org/10.54060/jieee/002.02.009.

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Abstract (sommario):
Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positi
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