Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Artificial Intelligence
Title: Knowledge-driven stock trend prediction and explanation via temporal convolutional network
Abstract: Deep neural networks have achieved promising results in stock trend prediction. However, most of these models have two common drawbacks, including (i) current methods are not sensitive enough to abrupt changes of stock trend, and (ii) forecasting results are not interpretable for humans. To address these two problems, we propose a novel Knowledge-Driven Temporal Convolutional Network (KDTCN) for stock trend prediction and explanation. Firstly, we extract structured events from financial news, and utilize external knowledge from knowledge graph to obtain event embeddings. Then, we combine event embeddings and price values together to forecast stock trend. We evaluate the prediction accuracy to show how knowledge-driven events work on abrupt changes. We also visualize the effect of events and linkage among events based on knowledge graph, to explain why knowledge-driven events are common sources of abrupt changes. Experiments demonstrate that KDTCN can (i) react to abrupt changes much faster and outperform state-of-the-art methods on stock datasets, as well as (ii) facilitate the explanation of prediction particularly with abrupt changes.
Extracted statement — Machine Learning Model Integration: external
Research field: Artificial Intelligence
Title: Knowledge-based transfer learning explanation
Abstract: Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at utilizing knowledge from one learning domain (i.e., a pair of dataset and prediction task) to enhance prediction model training in another learning domain. In this paper , we propose an ontology-based approach for human-centric explanation of transfer learning. Three kinds of knowledge-based explanatory evidence, with different granularities, including general factors, particular narrators and core contexts are first proposed and then inferred with both local ontologies and external knowledge bases. The evaluation with US flight data and DB-pedia has presented their confidence and availability in explaining the transferability of feature representation in flight departure delay forecasting.
Extracted statement — Machine Learning Model Integration: external
Research field: Artificial Intelligence
Title: SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection
Abstract: Lexical Semantic Change detection, i.e., the task of identifying words that change meaning over time, is a very active research area, with applications in NLP, lexicography, and linguistics. Evaluation is currently the most pressing problem in Lexical Semantic Change detection, as no gold standards are available to the community, which hinders progress. We present the results of the first shared task that addresses this gap by providing researchers with an evaluation framework and manually annotated, high-quality datasets for English, German, Latin, and Swedish. 33 teams submitted 186 systems, which were evaluated on two subtasks.
Extracted statement — Languages: German
Research field: Artificial Intelligence
Title: UWB at SemEval-2020 Task 1: Lexical Semantic Change Detection
Abstract: In this paper, we describe our method for detection of lexical semantic change, i.e., word sense changes over time. We examine semantic differences between specific words in two corpora, chosen from different time periods, for English, German, Latin, and Swedish. Our method was created for the SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. We ranked 1st in Sub-task 1: binary change detection, and 4th in Sub-task 2: ranked change detection. We present our method which is completely unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later; computing a linear transformation between earlier and later spaces, using Canonical Correlation Analysis and orthogonal transformation;and measuring the cosines between the transformed vector for the target word from the earlier corpus and the vector for the target word in the later corpus.
Extracted statement — Languages: German
Research field: Artificial Intelligence
Title: Knowledge-driven stock trend prediction and explanation via temporal convolutional network
Abstract: Deep neural networks have achieved promising results in stock trend prediction. However, most of these models have two common drawbacks, including (i) current methods are not sensitive enough to abrupt changes of stock trend, and (ii) forecasting results are not interpretable for humans. To address these two problems, we propose a novel Knowledge-Driven Temporal Convolutional Network (KDTCN) for stock trend prediction and explanation. Firstly, we extract structured events from financial news, and utilize external knowledge from knowledge graph to obtain event embeddings. Then, we combine event embeddings and price values together to forecast stock trend. We evaluate the prediction accuracy to show how knowledge-driven events work on abrupt changes. We also visualize the effect of events and linkage among events based on knowledge graph, to explain why knowledge-driven events are common sources of abrupt changes. Experiments demonstrate that KDTCN can (i) react to abrupt changes much faster and outperform state-of-the-art methods on stock datasets, as well as (ii) facilitate the explanation of prediction particularly with abrupt changes.
Extracted statement — Machine Learning Input: raw
Research field: Artificial Intelligence
Title: Knowledge-based interactive postmining of association rules using ontologies
Abstract: In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. To overcome this drawback, several methods were proposed in the literature such as itemset concise representations, redundancy reduction, and postprocessing. However, being generally based on statistical information, most of these methods do not guarantee that the extracted rules are interesting for the user. Thus, it is crucial to help the decision-maker with an efficient postprocessing step in order to reduce the number of rules. This paper proposes a new interactive approach to prune and filter discovered rules. First, we propose to use ontologies in order to improve the integration of user knowledge in the postprocessing task. Second, we propose the Rule Schema formalism extending the specification language proposed by Liu et al. for user expectations. Furthermore, an interactive framework is designed to assist the user throughout the analyzing task. Applying our new approach over voluminous sets of rules, we were able, by integrating domain expert knowledge in the postprocessing step, to reduce the number of rules to several dozens or less. Moreover, the quality of the filtered rules was validated by the domain expert at various points in the interactive process.
Extracted statement — Machine Learning Input: raw
Research field: Artificial Intelligence
Title: Interpreting data mining results with linked data for learning analytics: motivation, case study and direction
Abstract: Learning Analytics by nature relies on computational information processing activities intended to extract from raw data some interesting aspects that can be used to obtain insights into the behaviours of learners, the design of learning experiences, etc. There is a large variety of computational techniques that can be employed, all with interesting properties, but it is the interpretation of their results that really forms the core of the analytics process. In this paper, we look at a specific data mining method, namely sequential pattern extraction, and we demonstrate an approach that exploits available linked open data for this interpretation task. Indeed, we show through a case study relying on data about students' enrolment in course modules how linked data can be used to provide a variety of additional dimensions through which the results of the data mining method can be explored, providing, at interpretation time, new input into the analytics process.
Extracted statement — Machine Learning Input: raw
Research field: Artificial Intelligence
Title: Automatic Chinese food identification and quantity estimation
Abstract: Computer-aided food identification and quantity estimation have caught more attention in recent years because of the growing concern of our health. The identification problem is usually defined as an image categorization or classification problem and several researches have been proposed. In this paper, we address the issues of feature descriptors in the food identification problem and introduce a preliminary approach for the quantity estimation using depth information. Sparse coding is utilized in the SIFT and Local binary pattern feature descriptors, and these features combined with gabor and color features are used to represent food items. A multi-label SVM classifier is trained for each feature, and these classifiers are combined with multi-class Adaboost algorithm. For evaluation, 50 categories of worldwide food are used, and each category contains 100 photographs from different sources, such as manually taken or from Internet web albums. An overall accuracy of 68.3% is achieved, and success at top-N candidates achieved 80.6%, 84.8%, and 90.9% accuracy accordingly when N equals 2, 3, and 5, thus making mobile application practical. The experimental results show that the proposed methods greatly improve the performance of original SIFT and LBP feature descriptors. On the other hand, for quantity estimation using depth information, a straight forward method is proposed for certain food, while transparent food ingredients such as pure water and cooked rice are temporarily excluded.
Extracted statement — Annotation: Label
Research field: Atomic, Molecular and Optical Physics
Title: Theoretical energies for the <i>n</i> = 1 and 2 states of the helium isoelectronic sequence up to <i>Z</i> = 100
Abstract: The unified method described previously for combining high-precision nonrelativistic variational calculations with relativistic and quantum electrodynamic corrections is applied to the 1s 2 1 S 0 , 1s2s 1 S 0 , 1s2s 3 S 1 , 1s2p 1 P 1 , and 1s2p 3 P 0,1,2 staters of helium-like ions. Detailed tabulations are presented for all ions in the range 2 ≤ Z ≤ 100 and are compared with a wide range of experimental data up to 34 Kr + . The results for 90 U + significantly alter the recent Lamb shift measurement of Munger and Gould from 70.4 ± 8.3 to 71.0 ± 8.3 eV, in comparison with a revised theoretical value of 74.3 ± 0.4 eV. The improved agreement is due to the inclusion of higher order two-electron corrections in the present work.
Extracted statement — Paper type: Theoretical
Research field: Biogerontology and Geriatric Medicine
Title: Respiratory Care Received by Individuals With Duchenne Muscular Dystrophy From 2000 to 2011
Abstract: BACKGROUND: Duchenne muscular dystrophy (DMD) causes progressive respiratory muscle weakness and decline in function, which can go undetected without monitoring. DMD respiratory care guidelines recommend scheduled respiratory assessments and use of respiratory assist devices. To determine the extent of adherence to these guidelines, we evaluated respiratory assessments and interventions among males with DMD in the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) from 2000 to 2011. METHODS: MD STARnet is a population-based surveillance system that identifies all individuals born during or after 1982 residing in Arizona, Colorado, Georgia, Hawaii, Iowa, and western New York with Duchenne or Becker muscular dystrophy. We analyzed MD STARnet respiratory care data for non-ambulatory adolescent males (12–17 y old) and men (≥18 y old) with DMD, assessing whether: (1) pulmonary function was measured twice yearly; (2) awake and asleep hypoventilation testing was performed at least yearly; (3) home mechanical insufflation-exsufflation, noninvasive ventilation, and tracheostomy/ventilators were prescribed; and (4) pulmonologists provided evaluations. RESULTS: During 2000–2010, no more than 50% of both adolescents and men had their pulmonary function monitored twice yearly in any of the years; 67% or fewer were assessed for awake and sleep hypoventilation yearly. Although the use of mechanical insufflation-exsufflation and noninvasive ventilation is probably increasing, prior use of these devices did not prevent all tracheostomies, and at least 18 of 29 tracheostomies were performed due to acute respiratory illnesses. Fewer than 32% of adolescents and men had pulmonologist evaluations in 2010–2011. CONCLUSIONS: Since the 2004 publication of American Thoracic Society guidelines, there have been few changes in pulmonary clinical practice. Frequencies of respiratory assessments and assist device use among males with DMD were lower than recommended in clinical guidelines. Collaboration of respiratory therapists and pulmonologists with clinicians caring for individuals with DMD should be encouraged to ensure access to the full spectrum of in-patient and out-patient pulmonary interventions.
Extracted statement — Subject Label: American
Research field: Biogerontology and Geriatric Medicine
Title: Respiratory Care Received by Individuals With Duchenne Muscular Dystrophy From 2000 to 2011
Abstract: BACKGROUND: Duchenne muscular dystrophy (DMD) causes progressive respiratory muscle weakness and decline in function, which can go undetected without monitoring. DMD respiratory care guidelines recommend scheduled respiratory assessments and use of respiratory assist devices. To determine the extent of adherence to these guidelines, we evaluated respiratory assessments and interventions among males with DMD in the Muscular Dystrophy Surveillance, Tracking, and Research Network (MD STARnet) from 2000 to 2011. METHODS: MD STARnet is a population-based surveillance system that identifies all individuals born during or after 1982 residing in Arizona, Colorado, Georgia, Hawaii, Iowa, and western New York with Duchenne or Becker muscular dystrophy. We analyzed MD STARnet respiratory care data for non-ambulatory adolescent males (12–17 y old) and men (≥18 y old) with DMD, assessing whether: (1) pulmonary function was measured twice yearly; (2) awake and asleep hypoventilation testing was performed at least yearly; (3) home mechanical insufflation-exsufflation, noninvasive ventilation, and tracheostomy/ventilators were prescribed; and (4) pulmonologists provided evaluations. RESULTS: During 2000–2010, no more than 50% of both adolescents and men had their pulmonary function monitored twice yearly in any of the years; 67% or fewer were assessed for awake and sleep hypoventilation yearly. Although the use of mechanical insufflation-exsufflation and noninvasive ventilation is probably increasing, prior use of these devices did not prevent all tracheostomies, and at least 18 of 29 tracheostomies were performed due to acute respiratory illnesses. Fewer than 32% of adolescents and men had pulmonologist evaluations in 2010–2011. CONCLUSIONS: Since the 2004 publication of American Thoracic Society guidelines, there have been few changes in pulmonary clinical practice. Frequencies of respiratory assessments and assist device use among males with DMD were lower than recommended in clinical guidelines. Collaboration of respiratory therapists and pulmonologists with clinicians caring for individuals with DMD should be encouraged to ensure access to the full spectrum of in-patient and out-patient pulmonary interventions.
Extracted statement — Subject Label: Clinical
Research field: Bioinformatics
Title: CeleST: Computer Vision Software for Quantitative Analysis of C. elegans Swim Behavior Reveals Novel Features of Locomotion
Abstract: In the effort to define genes and specific neuronal circuits that control behavior and plasticity, the capacity for high-precision automated analysis of behavior is essential. We report on comprehensive computer vision software for analysis of swimming locomotion of C. elegans, a simple animal model initially developed to facilitate elaboration of genetic influences on behavior. C. elegans swim test software CeleST tracks swimming of multiple animals, measures 10 novel parameters of swim behavior that can fully report dynamic changes in posture and speed, and generates data in several analysis formats, complete with statistics. Our measures of swim locomotion utilize a deformable model approach and a novel mathematical analysis of curvature maps that enable even irregular patterns and dynamic changes to be scored without need for thresholding or dropping outlier swimmers from study. Operation of CeleST is mostly automated and only requires minimal investigator interventions, such as the selection of videotaped swim trials and choice of data output format. Data can be analyzed from the level of the single animal to populations of thousands. We document how the CeleST program reveals unexpected preferences for specific swim “gaits” in wild-type C. elegans, uncovers previously unknown mutant phenotypes, efficiently tracks changes in aging populations, and distinguishes “graceful” from poor aging. The sensitivity, dynamic range, and comprehensive nature of CeleST measures elevate swim locomotion analysis to a new level of ease, economy, and detail that enables behavioral plasticity resulting from genetic, cellular, or experience manipulation to be analyzed in ways not previously possible.
Extracted statement — creates: CeleST
Research field: Bioinformatics
Title: Chaste: An Open Source C++ Library for Computational Physiology and Biology
Abstract: Chaste — Cancer, Heart And Soft Tissue Environment — is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high-performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cell-based simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs). Re-use of these components avoids the need for researchers to ‘re-invent the wheel’ with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular test-driven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at http://www.cs.ox.ac.uk/chaste, together with details of a mailing list and links to documentation and tutorials.
Extracted statement — deposits: Chaste
Research field: Chemistry
Title: One-step hydrothermal synthesis of N-doped TiO 2/C nanocomposites with high visible light photocatalytic activity
Abstract: N-doped TiO(2) nanoparticles modified with carbon (denoted N-TiO(2)/C) were successfully prepared by a facile one-pot hydrothermal treatment in the presence of L-lysine, which acts as a ligand to control the nanocrystal growth and as a source of nitrogen and carbon. As-prepared nanocomposites were characterized by thermogravimetric analysis (TGA), X-ray diffraction (XRD), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), Raman spectroscopy, ultraviolet-visible (UV-vis) diffuse reflectance spectroscopy, X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), electron paramagnetic resonance (EPR) spectra, and N(2) adsorption-desorption analysis. The photocatalytic activities of the as-prepared photocatalysts were measured by the degradation of methyl orange (MO) under visible light irradiation at λ≥ 400 nm. The results show that N-TiO(2)/C nanocomposites increase absorption in the visible light region and exhibit a higher photocatalytic activity than pure TiO(2), commercial P25 and previously reported N-doped TiO(2) photocatalysts. We have demonstrated that the nitrogen was doped into the lattice and the carbon species were modified on the surface of the photocatalysts. N-doping narrows the band gap and C-modification enhances the visible light harvesting and accelerates the separation of the photo-generated electrons and holes. As a consequence, the photocatalytic activity is significantly improved. The molar ratio of L-lysine/TiCl(4) and the pH of the hydrothermal reaction solution are important factors affecting the photocatalytic activity of the N-TiO(2)/C; the optimum molar ratio of L-lysine/TiCl(4) is 8 and the optimum pH is ca. 4, at which the catalyst exhibits the highest reactivity. Our findings demonstrate that the as-obtained N-TiO(2)/C photocatalyst is a better and more promising candidate than well studied N-doped TiO(2) alternatives as visible light photocatalysts for potential applications in environmental purification.
Extracted statement — chemical doping method: hydrothermal
Research field: Chemistry
Title: Formation of New Structures and Their Synergistic Effects in Boron and Nitrogen Codoped TiO2 for Enhancement of Photocatalytic Performance
Abstract: A novel double hydrothermal method to prepare the boron and nitrogen codoped TiO2 is developed. Two different ways have been used for the synthesis of the catalysts, one through the addition of boron followed by nitrogen, and the other through the addition of nitrogen first and then by boron. The X-ray photoelectron spectroscopy analysis indicates the synergistic effect of boron and nitrogen with the formation of Ti−B−N−Ti and Ti−N−B−O compounds on the surface of catalysts when nitrogen is introduced to the materials first. When the boron is added first, only Ti−N−B−O species occurs on the surface of catalysts. The above two compounds are all thought to enhance the photocatalytic activities of codoped TiO2. Density functional theory simulations are also performed to investigate the B−N synergistic effect. For the (101) surface, the formation of Ti−B−N−Ti structures gives rise to the localized states within the TiO2 band gap.
Extracted statement — chemical doping method: hydrothermal