scholarly journals Open science resources for the discovery and analysis of Tara Oceans Data

2015 ◽  
Author(s):  
Stéphane Pesant ◽  
Fabrice Not ◽  
Marc Picheral ◽  
Stefanie Kandels-Lewis ◽  
Noan Le Bescot ◽  
...  
Keyword(s):  
State Of The Art ◽  
Open Science ◽  
Environmental Data ◽  
Morphological Data ◽  
Data Sets ◽  
Ecological Context ◽  
Biogeographic Provinces ◽  
Collaborative Annotation ◽  
On Line ◽  
Extensive Collection

The Tara Oceans expedition (2009-2013) sampled contrasting ecosystems of the world oceans, collecting environmental data and plankton, from viruses to metazoans, for later analysis using modern sequencing and state-of-the-art imaging technologies. It surveyed 210 ecosystems in 20 biogeographic provinces, collecting over 35000 samples of seawater and plankton. The interpretation of such an extensive collection of samples in their ecological context requires means to explore, assess and access raw and validated data sets. To address this challenge, the Tara Oceans Consortium offers open science resources, including the use of open access archives for nucleotides (ENA) and for environmental, biogeochemical, taxonomic and morphological data (PANGAEA), and the development of on line discovery tools and collaborative annotation tools for sequences and images. Here, we present an overview of Tara Oceans Data, and we provide detailed registries (data sets) of all campaigns (from port-to-port), stations and sampling events.

scholarly journals Using control charts for on-line video summarisation

2019 ◽  
Vol 277 ◽  
pp. 01012 ◽  
Author(s):  
Clare E. Matthews ◽  
Paria Yousefi ◽  
Ludmila I. Kuncheva
Keyword(s):  
Feature Extraction ◽  
Control Chart ◽  
Control Charts ◽  
State Of The Art ◽  
Synthetic Data ◽  
New Method ◽  
Data Sets ◽  
On Line ◽  
Memory Constraints ◽  
Video Summarisation

Many existing methods for video summarisation are not suitable for on-line applications, where computational and memory constraints mean that feature extraction and frame selection must be simple and efficient. Our proposed method uses RGB moments to represent frames, and a control-chart procedure to identify shots from which keyframes are then selected. The new method produces summaries of higher quality than two state-of-the-art on-line video summarisation methods identified as the best among nine such methods in our previous study. The summary quality is measured against an objective ideal for synthetic data sets, and compared to user-generated summaries of real videos.

scholarly journals Authorship Attribution with Topic Models

2014 ◽  
Vol 40 (2) ◽  
pp. 269-310 ◽  
Author(s):  
Yanir Seroussi ◽  
Ingrid Zukerman ◽  
Fabian Bohnert
Keyword(s):  
State Of The Art ◽  
Topic Models ◽  
Experimental Results ◽  
Authorship Attribution ◽  
Data Sets ◽  
New Model ◽  
On Line ◽  
Art Performance

Authorship attribution deals with identifying the authors of anonymous texts. Traditionally, research in this field has focused on formal texts, such as essays and novels, but recently more attention has been given to texts generated by on-line users, such as e-mails and blogs. Authorship attribution of such on-line texts is a more challenging task than traditional authorship attribution, because such texts tend to be short, and the number of candidate authors is often larger than in traditional settings. We address this challenge by using topic models to obtain author representations. In addition to exploring novel ways of applying two popular topic models to this task, we test our new model that projects authors and documents to two disjoint topic spaces. Utilizing our model in authorship attribution yields state-of-the-art performance on several data sets, containing either formal texts written by a few authors or informal texts generated by tens to thousands of on-line users. We also present experimental results that demonstrate the applicability of topical author representations to two other problems: inferring the sentiment polarity of texts, and predicting the ratings that users would give to items such as movies.

scholarly journals Fostering Open Science at WSL with the EnviDat Environmental Data Portal

2018 ◽  
Author(s):  
Ionut Iosifescu Enescu ◽  
Marielle Fraefel ◽  
Gian-Kasper Plattner ◽  
Lucia Espona-Pernas ◽  
Dominik Haas-Artho ◽  
...  
Keyword(s):  
Open Science ◽  
Research Data ◽  
Environmental Data ◽  
Data Sets ◽  
Institutional Research ◽  
Data Set ◽  
Digital Resources ◽  
Federal Institute ◽  
Data Policy ◽  
Data Portal

EnviDat is the institutional research data portal of the Swiss Federal Institute for Forest, Snow and Landscape WSL. The portal is designed to provide solutions for efficient, unified and managed access to the WSL’s comprehensive reservoir of monitoring and research data, in accordance with the WSL data policy. Through EnviDat, WSL is fostering open science, making curated, quality-controlled, publication-ready research data accessible. Data producers can document author contributions for a particular data set through the EnviDat-DataCRediT taxonomy. The publication of research data sets can be complemented with additional digital resources, such as, e.g., supplementary documentation, processing software or detailed descriptions of code (i.e. as Jupyter Notebooks). The EnviDat Team is working towards generic solutions for enhancing open science, in line with WSL’s commitment to accessible research data.

scholarly journals Fostering Open Science at WSL with the EnviDat Environmental Data Portal

2018 ◽  
Author(s):  
Ionut Iosifescu Enescu ◽  
Marielle Fraefel ◽  
Gian-Kasper Plattner ◽  
Lucia Espona-Pernas ◽  
Dominik Haas-Artho ◽  
...  
Keyword(s):  
Open Science ◽  
Research Data ◽  
Environmental Data ◽  
Data Sets ◽  
Institutional Research ◽  
Data Set ◽  
Digital Resources ◽  
Federal Institute ◽  
Data Policy ◽  
Data Portal

EnviDat is the institutional research data portal of the Swiss Federal Institute for Forest, Snow and Landscape WSL. The portal is designed to provide solutions for efficient, unified and managed access to the WSL’s comprehensive reservoir of monitoring and research data, in accordance with the WSL data policy. Through EnviDat, WSL is fostering open science, making curated, quality-controlled, publication-ready research data accessible. Data producers can document author contributions for a particular data set through the EnviDat-DataCRediT taxonomy. The publication of research data sets can be complemented with additional digital resources, such as, e.g., supplementary documentation, processing software or detailed descriptions of code (i.e. as Jupyter Notebooks). The EnviDat Team is working towards generic solutions for enhancing open science, in line with WSL’s commitment to accessible research data.

Confidentiality of Statistical Records: A Threat-Monitoring Scheme for On Line Dialogue

1976 ◽  
Vol 15 (01) ◽  
pp. 36-42 ◽  
Author(s):  
J. Schlörer
Keyword(s):  
Statistical Data ◽  
Cost Benefit ◽  
Data Bank ◽  
High Ratio ◽  
Point Of View ◽  
Data Sets ◽  
Monitoring Scheme ◽  
Access Controls ◽  
On Line ◽  
Bona Fide

From a statistical data bank containing only anonymous records, the records sometimes may be identified and then retrieved, as personal records, by on line dialogue. The risk mainly applies to statistical data sets representing populations, or samples with a high ratio n/N. On the other hand, access controls are unsatisfactory as a general means of protection for statistical data banks, which should be open to large user communities. A threat monitoring scheme is proposed, which will largely block the techniques for retrieval of complete records. If combined with additional measures (e.g., slight modifications of output), it may be expected to render, from a cost-benefit point of view, intrusion attempts by dialogue valueless, if not absolutely impossible. The bona fide user has to pay by some loss of information, but considerable flexibility in evaluation is retained. The proposal of controlled classification included in the scheme may also be useful for off line dialogue systems.

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

Selection of one-dimensional sedimentation: models for on-line use

1995 ◽  
Vol 31 (2) ◽  
pp. 193-204 ◽  
Author(s):  
Koen Grijspeerdt ◽  
Peter Vanrolleghem ◽  
Willy Verstraete
Keyword(s):  
Steady State ◽  
Selection Criteria ◽  
Data Sets ◽  
Concentration Profiles ◽  
A Posteriori ◽  
One Dimensional ◽  
On Line ◽  
Dynamic Concentration ◽  
Selection Of ◽  
Modelling Task

A comparative study of several recently proposed one-dimensional sedimentation models has been made. This has been achieved by fitting these models to steady-state and dynamic concentration profiles obtained in a down-scaled secondary decanter. The models were evaluated with several a posteriori model selection criteria. Since the purpose of the modelling task is to do on-line simulations, the calculation time was used as one of the selection criteria. Finally, the practical identifiability of the models for the available data sets was also investigated. It could be concluded that the model of Takács et al. (1991) gave the most reliable results.

scholarly journals A novel optimal multi-pattern matching method with wildcards for DNA sequence

2021 ◽  
Vol 29 ◽  
pp. 115-124
Author(s):  
Xinlu Wang ◽  
Ahmed A.F. Saif ◽  
Dayou Liu ◽  
Yungang Zhu ◽  
Jon Atli Benediktsson
Keyword(s):  
Dna Sequence ◽  
Pattern Matching ◽  
Health Informatics ◽  
State Of The Art ◽  
Machine Language ◽  
Data Sets ◽  
Fundamental Issue ◽  
Matching Method ◽  
Dna Sequence Alignment ◽  
The Given

BACKGROUND: DNA sequence alignment is one of the most fundamental and important operation to identify which gene family may contain this sequence, pattern matching for DNA sequence has been a fundamental issue in biomedical engineering, biotechnology and health informatics. OBJECTIVE: To solve this problem, this study proposes an optimal multi pattern matching with wildcards for DNA sequence. METHODS: This proposed method packs the patterns and a sliding window of texts, and the window slides along the given packed text, matching against stored packed patterns. RESULTS: Three data sets are used to test the performance of the proposed algorithm, and the algorithm was seen to be more efficient than the competitors because its operation is close to machine language. CONCLUSIONS: Theoretical analysis and experimental results both demonstrate that the proposed method outperforms the state-of-the-art methods and is especially effective for the DNA sequence.

scholarly journals gbt-HIPS: Explaining the Classifications of Gradient Boosted Tree Ensembles

2021 ◽  
Vol 11 (6) ◽  
pp. 2511
Author(s):  
Julian Hatwell ◽  
Mohamed Medhat Gaber ◽  
R. Muhammad Atif Azad
Keyword(s):  
State Of The Art ◽  
Heuristic Method ◽  
Good Explanation ◽  
Classification Rule ◽  
Data Sets ◽  
Classification Models ◽  
Boundary Values ◽  
Class Label ◽  
Input Space ◽  
Boosted Tree

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explanandum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16–0.75) and precision (0.85–0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation.

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

Sign in / Sign up

Export Citation Format

Share Document