scholarly journals The Graph Reasoning Approach Based on the Dynamic Knowledge Auxiliary for Complex Fact Verification

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1472
Author(s):  
Yongyue Wang ◽  
Chunhe Xia ◽  
Chengxiang Si ◽  
Chongyu Zhang ◽  
Tianbo Wang
Keyword(s):  
Common Sense ◽  
Negative Impact ◽  
Background Knowledge ◽  
Small Sample ◽  
Training Data ◽  
Reasoning Ability ◽  
Auxiliary Graph ◽  
Graph Neural Networks ◽  
The Given ◽  
Conceptual Relationship

Complex fact verification (FV) requires fusing scattered sequences and performing multi-hop reasoning over these composed sequences. Recently, by employing some FV models, knowledge is obtained from context to support the reasoning process based on pretrained models (e.g., BERT, XLNET), and this model outperforms previous out-of-the-art FV models. In practice, however, the limited training data cannot provide enough background knowledge for FV tasks. Once the background knowledge changed, the pretrained models’ parameters cannot be updated. Additionally, noise against common sense cannot be accurately filtered out due to the lack of necessary knowledge, which may have a negative impact on the reasoning progress. Furthermore, existing models often wrongly label the given claims as ‘not enough information’ due to the lack of necessary conceptual relationship between pieces of evidence. In the present study, a Dynamic Knowledge Auxiliary Graph Reasoning (DKAR) approach is proposed for incorporating external background knowledge in the current FV model, which explicitly identifies and fills the knowledge gaps between provided sources and the given claims, to enhance the reasoning ability of graph neural networks. Experiments show that DKAR put forward in this study can be combined with specific and discriminative knowledge to guide the FV system to successfully overcome the knowledge-gap challenges and achieve improvement in FV tasks. Furthermore, DKAR is adopted to complete the FV task on the Fake NewsNet dataset, showing outstanding advantages in a small sample and heterogeneous web text source.

scholarly journals k-Nearest Neighbor Learning with Graph Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 830
Author(s):  
Seokho Kang
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Weighting Function ◽  
High Sensitivity ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Main Challenge ◽  
Benchmark Datasets ◽  
Graph Neural Networks

k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance function, and the weighting function. To improve the robustness to hyperparameters, this study presents a novel kNN learning method based on a graph neural network, named kNNGNN. Given training data, the method learns a task-specific kNN rule in an end-to-end fashion by means of a graph neural network that takes the kNN graph of an instance to predict the label of the instance. The distance and weighting functions are implicitly embedded within the graph neural network. For a query instance, the prediction is obtained by performing a kNN search from the training data to create a kNN graph and passing it through the graph neural network. The effectiveness of the proposed method is demonstrated using various benchmark datasets for classification and regression tasks.

scholarly journals Characterization of the Effectiveness of Reporting Lists of Small Feature Sets Relative to the Accuracy of the Prior Biological Knowledge

2010 ◽  
Vol 9 ◽  
pp. CIN.S4020 ◽  
Author(s):  
Chen Zhao ◽  
Michael L. Bittner ◽  
Robert S. Chapkin ◽  
Edward R. Dougherty
Keyword(s):  
Small Sample ◽  
Training Data ◽  
Small Samples ◽  
Classification Error ◽  
Biological Knowledge ◽  
Feature Sets ◽  
Good Feature ◽  
Prior Biological Knowledge ◽  
Selection Algorithms ◽  
Power Curves

When confronted with a small sample, feature-selection algorithms often fail to find good feature sets, a problem exacerbated for high-dimensional data and large feature sets. The problem is compounded by the fact that, if one obtains a feature set with a low error estimate, the estimate is unreliable because training-data-based error estimators typically perform poorly on small samples, exhibiting optimistic bias or high variance. One way around the problem is limit the number of features being considered, restrict features sets to sizes such that all feature sets can be examined by exhaustive search, and report a list of the best performing feature sets. If the list is short, then it greatly restricts the possible feature sets to be considered as candidates; however, one can expect the lowest error estimates obtained to be optimistically biased so that there may not be a close-to-optimal feature set on the list. This paper provides a power analysis of this methodology; in particular, it examines the kind of results one should expect to obtain relative to the length of the list and the number of discriminating features among those considered. Two measures are employed. The first is the probability that there is at least one feature set on the list whose true classification error is within some given tolerance of the best feature set and the second is the expected number of feature sets on the list whose true errors are within the given tolerance of the best feature set. These values are plotted as functions of the list length to generate power curves. The results show that, if the number of discriminating features is not too small—that is, the prior biological knowledge is not too poor—then one should expect, with high probability, to find good feature sets. Availability: companion website at http://gsp.tamu.edu/Publications/supplementary/zhao09a/

Mixed-gender ownership and financial performance of SMEs in South Africa

2016 ◽  
Vol 8 (2) ◽  
pp. 117-136 ◽  
Author(s):  
Beatrice Desiree Simo Kengne
Keyword(s):  
South Africa ◽  
Financial Performance ◽  
Negative Impact ◽  
Firm Survival ◽  
Industrial Sector ◽  
Individual Characteristics ◽  
Small Sample ◽  
Gender Composition ◽  
Content Type ◽  
Mixed Gender

Purpose The purpose of this paper is to investigate whether the presence of women among owner-stakeholders affects firms’ financial performance. Particularly, it extends the corporate governance literature by linking stakeholder theory and gender differences to explain why gender composition of ownership matters for firms’ performance. As the management of small and medium-scale enterprises (SMEs) revolves around owner-managers and their individual characteristics that are likely to affect their achievements, the study further investigates the relationship between the gender composition of ownership and the firm survival. Design/methodology/approach Using survey data on SMEs for 2007 and 2010, this study uses a panel-level heteroskedasticity technique and a probit methodology to assess the effect women’s presence among owners may exert on SMEs performance and survival, respectively. Findings Results indicate that firms jointly owned by men and women appear to perform better than those owned by men although the presence of women among owners does not correlate with firm survival. Research limitations/implications While the findings of this study shed some light on the performance impact of gender composition of firm ownership, reports based on the presence of women among owners may not present the full picture. Whether the ownership is shared equally between different genders might provide further insides on the magnitude and/or robustness of such effect. Moreover, a small sample period (T = 2) was used to analyse a single industrial sector (manufacturing), and even though the Hausman test confirmed the use of random-effects specification, caution should be taken when generalizing the findings to other cases. Practical implications The findings suggest that the leadership in mixed-gender context propels a perspective of women as a valuable resource within SMEs, but relying on it to sustain the survival would be unwise. Social implications South Africa scores particularly high on positive actions towards women entrepreneurship, and this is compounded in the SMEs sector by managerial attitudes that could offer positive developments for women. Originality/value The positive and significant relationship between women’s presence among owners and SMEs financial performance in South Africa complements the almost exclusively reported negative impact of gender diversity on firm performance. Consequently, mixed-gender owners’ team can be used as a fulcrum to promote SMEs growth in South Africa.

scholarly journals Exemplar Guided Neural Dialogue Generation

2020 ◽  
Author(s):  
Hengyi Cai ◽  
Hongshen Chen ◽  
Yonghao Song ◽  
Xiaofang Zhao ◽  
Dawei Yin
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Training Data ◽  
Small Subset ◽  
Generation Model ◽  
Retrieval Model ◽  
Training Set ◽  
Dialogue Model ◽  
Quantitative Metrics ◽  
The Given

Humans benefit from previous experiences when taking actions. Similarly, related examples from the training data also provide exemplary information for neural dialogue models when responding to a given input message. However, effectively fusing such exemplary information into dialogue generation is non-trivial: useful exemplars are required to be not only literally-similar, but also topic-related with the given context. Noisy exemplars impair the neural dialogue models understanding the conversation topics and even corrupt the response generation. To address the issues, we propose an exemplar guided neural dialogue generation model where exemplar responses are retrieved in terms of both the text similarity and the topic proximity through a two-stage exemplar retrieval model. In the first stage, a small subset of conversations is retrieved from a training set given a dialogue context. These candidate exemplars are then finely ranked regarding the topical proximity to choose the best-matched exemplar response. To further induce the neural dialogue generation model consulting the exemplar response and the conversation topics more faithfully, we introduce a multi-source sampling mechanism to provide the dialogue model with both local exemplary semantics and global topical guidance during decoding. Empirical evaluations on a large-scale conversation dataset show that the proposed approach significantly outperforms the state-of-the-art in terms of both the quantitative metrics and human evaluations.

COMBINING GENERALIZED NMF AND DISCRIMINATIVE MIXTURE MODELS FOR CLASSIFICATION OF GENE EXPRESSION DATA

2008 ◽  
Vol 22 (08) ◽  
pp. 1587-1598 ◽  
Author(s):  
WEIXIANG LIU ◽  
KEHONG YUAN ◽  
JIAN WU ◽  
DATIAN YE ◽  
ZHEN JI ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mixture Model ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Data Classification ◽  
Small Sample ◽  
Training Data ◽  
Microarray Data Analysis ◽  
Expression Data

Classification of gene expression samples is a core task in microarray data analysis. How to reduce thousands of genes and to select a suitable classifier are two key issues for gene expression data classification. This paper introduces a framework on combining both feature extraction and classifier simultaneously. Considering the non-negativity, high dimensionality and small sample size, we apply a discriminative mixture model which is designed for non-negative gene express data classification via non-negative matrix factorization (NMF) for dimension reduction. In order to enhance the sparseness of training data for fast learning of the mixture model, a generalized NMF is also adopted. Experimental results on several real gene expression datasets show that the classification accuracy, stability and decision quality can be significantly improved by using the generalized method, and the proposed method can give better performance than some previous reported results on the same datasets.

INFLUENCE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES ON DEMOCRATIC PROCEDURES

2021 ◽  
Vol 3 ◽  
pp. 16-21
Author(s):  
S. FURS ◽  
Keyword(s):  
Artificial Intelligence ◽  
Negative Impact ◽  
Social Medium ◽  
Potential Risks ◽  
The Given

The article considers the specifics of the artificial intelligence (AI) technologies implementation and adaptation into social medium; it shows the interaction between the given process and democratic procedures. The author of the article emphasizes the fact that, in addition to the powerful results, AI technologies bear the potential risks to democratic procedures that are not studied enough. These risks result from the openness of AI technology in terms of purposes of use and application areas. To neutralize its negative impact and manifestation in future, an active study of this problem is required within the framework of the regulatory sphere. The article is dedicated to the consideration of this issue.

scholarly journals Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2019-315723
Author(s):  
Tan Hung Pham ◽  
Sripad Krishna Devalla ◽  
Aloysius Ang ◽  
Zhi-Da Soh ◽  
Alexandre H Thiery ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Objective Assessment ◽  
Anterior Segment ◽  
Angle Closure Glaucoma ◽  
Training Data ◽  
Angle Closure ◽  
Optical Coherence ◽  
Segmentation Approach ◽  
Reliable Quantification ◽  
The Given

Background/AimsAccurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma.MethodIn this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble of DCNNs for the segmentation of AS structures (iris, corneosclera shell adn anterior chamber) was developed. Based on the results of two previous processes, an algorithm to automatically quantify clinically important measurements were created. 200 images from 58 patients (100 eyes) were used for testing.ResultsWith limited training data, the DCNN was able to detect the scleral spur on unseen anterior segment optical coherence tomography (ASOCT) images as accurately as an experienced ophthalmologist on the given test dataset and simultaneously isolated the AS structures with a Dice coefficient of 95.7%. We then automatically extracted eight clinically relevant ASOCT measurements and proposed an automated quality check process that asserts the reliability of these measurements. When combined with an OCT machine capable of imaging multiple radial sections, the algorithms can provide a more complete objective assessment. The total segmentation and measurement time for a single scan is less than 2 s.ConclusionThis is an essential step towards providing a robust automated framework for reliable quantification of ASOCT scans, for applications in the diagnosis and management of angle-closure glaucoma.

scholarly journals Complex approach to organizations’ capital assets reproduction

2020 ◽  
Vol 157 ◽  
pp. 04026 ◽  
Author(s):  
Aleksandr Birjukov ◽  
Evgeniy Dobryshkin ◽  
Yurii Birjukov ◽  
Vladimir Tishchenko
Keyword(s):  
Working Conditions ◽  
Negative Impact ◽  
Technical Condition ◽  
Joint Estimation ◽  
Mathematical Apparatus ◽  
Production Processes ◽  
Capital Assets ◽  
Industrial Buildings ◽  
Complex Approach ◽  
The Given

Effective production activities of organizations is impossible without the concept implementation of the constant reproduction of capital assets, a significant part of which is represented by buildings and structures for various functional purposes. Increased deterioration of industrial buildings does not allow to solve such important tasks as improvement and automation of production processes in their entirety, and has a negative impact on the working conditions and safety of personnel. The analysis of scientific and normative literature is showed that the issue under consideration requires further research. Author’s approach to the reproduction of capital assets, the use of which allows to increase the management decisions efficiency on the basis of a complex of tasks for the joint estimation of damage and deterioration, planning of works under the given constraints with the use of mathematical apparatus and technological solutions for monitoring the technical condition of buildings is presented in the article.

scholarly journals SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

2020 ◽  
Author(s):  
Xiaoyu Lu ◽  
Szu-Wei Tu ◽  
Wennan Chang ◽  
Changlin Wan ◽  
Jiashi Wang ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Cell Types ◽  
Small Sample ◽  
Training Data ◽  
Mouse Tissue ◽  
Marker Genes ◽  
Specific Cell ◽  
Cell Type ◽  
Data Set ◽  
Transcriptomics Data

Abstract Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

Impact of the Slurry Pond of the Tyrnyauz Tungsten-Molybdenum Complex on the Mountain Territories Environment

2018 ◽  
Vol 931 ◽  
pp. 1070-1075 ◽  
Author(s):  
Buzgigit M. Huchunayev ◽  
Oksana O. Dakhova ◽  
Svetlana A. Bekkiyeva ◽  
Svetlana B. Hatefova
Keyword(s):  
Negative Impact ◽  
Scientific Work ◽  
Land Resources ◽  
Molybdenum Complex ◽  
Atmospheric Air ◽  
Impact Reduction ◽  
The Given ◽  
Made In

The results of the Tyrnyauz tungsten-molybdenum Plant (TTMP) slurry pond settler environment impacts assessment are given in this scientific work, and the recommendations about the negative impact reduction on the environment are made. In the given work the characteristics of the environment state are investigated: atmospheric air, water objects and land resources.

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