scholarly journals Regularized Simple Graph Convolution (SGC) for improved interpretability of large datasets

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Phuong Pho ◽  
Alexander V. Mantzaris
Keyword(s):  
Large Datasets ◽  
Journal Articles ◽  
Relevant Research ◽  
Research Papers ◽  
Ongoing Research ◽  
Network Information ◽  
Research Task ◽  
Data Points ◽  
Time Required

Abstract Classification of data points which correspond to complex entities such as people or journal articles is a ongoing research task. Notable applications are recommendation systems for customer behaviors based upon their features or past purchases and in academia labeling relevant research papers in order to reduce the reading time required. The features that can be extracted are many and result in large datasets which are a challenge to process with complex machine learning methodologies. There is also an issue on how this is presented and how to interpret the parameterizations beyond the classification accuracies. This work shows how the network information contained in an adjacency matrix allows improved classification of entities through their associations and how the framework of the SGC provide an expressive and fast approach. The proposed regularized SGC incorporates shrinkage upon three different aspects of the projection vectors to reduce the number of parameters, the size of the parameters and the directions between the vectors to produce more meaningful interpretations.

Liquidity of financial markets: a review

2019 ◽  
Vol 37 (2) ◽  
pp. 201-227 ◽  
Author(s):  
Abhinava Tripathi ◽  
Alok Dixit ◽  
Vipul Vipul
Keyword(s):  
Emerging Markets ◽  
Financial Markets ◽  
Return Predictability ◽  
Research Papers ◽  
Ongoing Research ◽  
Content Type ◽  
Research Areas ◽  
Comprehensive Collection ◽  
Depth Analysis

Purpose The purpose of this study is to systematically review and analyze the literature in the area of liquidity of financial markets. The study summarizes the key findings and approaches and highlights the research gaps in the extant literature. Design/methodology/approach A variety of reputed databases are utilized to select 100 research papers, from a large pool of nearly 3,000 research papers spanning between 1972 and 2018 using systematic literature review methodology. The selected research papers are organized to provide an in-depth analysis and an account of the ongoing research in the area of liquidity. The study uses bibliometric network visualization and word-cloud analyses to compile and analyze the literature. Findings The study summarizes the recent approaches in the liquidity research on aspects such as methodologies followed, variables applied, sub-areas covered, and the types of economies and markets covered. The article shows that the literature on liquidity in the emerging markets (e.g. China and India) is deficient. Overall, the following research areas related to liquidity need further exploration in the context of emerging markets: liquidity beyond the best bid-ask quotes, intraday return predictability using microstructure variables (e.g. order imbalances), impact of algorithmic-trading and volatility of liquidity. Originality/value To the best of authors’ knowledge, in the recent past, a detailed account of the literature on liquidity has not been published. It provides a comprehensive collection and classification of the literature on the liquidity of financial markets. This would be helpful to the future researchers, academics and practitioners in the area of financial markets.

Investigation of the Fourier Transform for Analyzing Spectroscopic Data by Computerized Learning Machines

1971 ◽  
Vol 25 (2) ◽  
pp. 203-207 ◽  
Author(s):  
L. E. Wangen ◽  
N. M. Frew ◽  
T. L. Isenhour ◽  
P. C. Jurs
Keyword(s):  
Fourier Transform ◽  
Data Storage ◽  
Spectroscopic Data ◽  
Weighted Average ◽  
Learning Machines ◽  
Frequency Representation ◽  
Data Points ◽  
The Fourier Transform ◽  
Time Required

This paper investigates the use of the fast Fourier transform as an aid in the analysis and classification of spectroscopic data. The pattern obtained after transformation is viewed as a weighted average and/or as a frequency representation of the original spectroscopic data. In pattern recognition the Fourier transform allows a different (i.e., a frequency) representation of the data which may prove more amenable to linear separation according to various categories of the patterns. The averaging property means that the information in each dimension of the original pattern is distributed over all dimensions in the pattern resulting from the Fourier transformation. Hence the arbitrary omission or loss of data points in the Fourier spectrum has less effect on the original spectrum. This property is exploited for reducing the dimensionality of the Fourier data so as to minimize data storage requirements and the time required for development of pattern classifiers for categorization of the data. Examples of applications are drawn from low resolution mass spectrometry.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

scholarly journals CSO Classifier 3.0: a scalable unsupervised method for classifying documents in terms of research topics

2021 ◽  
Author(s):  
Angelo Salatino ◽  
Francesco Osborne ◽  
Enrico Motta
Keyword(s):  
Computer Science ◽  
Digital Libraries ◽  
Gold Standard ◽  
Research Trends ◽  
Alternative Methods ◽  
Relevant Research ◽  
Research Topics ◽  
Research Papers ◽  
Research Areas ◽  
Unsupervised Approach

AbstractClassifying scientific articles, patents, and other documents according to the relevant research topics is an important task, which enables a variety of functionalities, such as categorising documents in digital libraries, monitoring and predicting research trends, and recommending papers relevant to one or more topics. In this paper, we present the latest version of the CSO Classifier (v3.0), an unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive taxonomy of research areas in the field of Computer Science. The CSO Classifier takes as input the textual components of a research paper (usually title, abstract, and keywords) and returns a set of research topics drawn from the ontology. This new version includes a new component for discarding outlier topics and offers improved scalability. We evaluated the CSO Classifier on a gold standard of manually annotated articles, demonstrating a significant improvement over alternative methods. We also present an overview of applications adopting the CSO Classifier and describe how it can be adapted to other fields.

scholarly journals BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Olga Majewska ◽  
Charlotte Collins ◽  
Simon Baker ◽  
Jari Björne ◽  
Susan Windisch Brown ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Model Performance ◽  
Representation Learning ◽  
Verb Classes ◽  
Expert Annotation ◽  
Biomedical Texts ◽  
Time Required ◽  
Verb Meaning

Abstract Background Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-related knowledge have been shown to boost model performance in different Natural Language Processing (NLP) tasks where accurate handling of verb meaning and behaviour is critical. The costliness and time required for manual lexicon construction has been a major obstacle to porting the benefits of such resources to NLP in specialised domains, such as biomedicine. To address this issue, we combine a neural classification method with expert annotation to create BioVerbNet. This new resource comprises 693 verbs assigned to 22 top-level and 117 fine-grained semantic-syntactic verb classes. We make this resource available complete with semantic roles and VerbNet-style syntactic frames. Results We demonstrate the utility of the new resource in boosting model performance in document- and sentence-level classification in biomedicine. We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class. The BioVerbNet knowledge-aware embeddings surpass the non-specialised baseline by a significant margin on both tasks. Conclusion This work introduces the first large, annotated semantic-syntactic classification of biomedical verbs, providing a detailed account of the annotation process, the key differences in verb behaviour between the general and biomedical domain, and the design choices made to accurately capture the meaning and properties of verbs used in biomedical texts. The demonstrated benefits of leveraging BioVerbNet in text classification suggest the resource could help systems better tackle challenging NLP tasks in biomedicine.

scholarly journals Towards a Unified Sentiment Lexicon Based on Graphics Processing Units

2014 ◽  
Vol 2014 ◽  
pp. 1-19
Author(s):  
Liliana Ibeth Barbosa-Santillán ◽  
Inmaculada Álvarez-de-Mon y-Rego
Keyword(s):  
Graphics Processing Units ◽  
Pearson Correlation ◽  
Lexical Entry ◽  
A Value ◽  
Sentiment Lexicon ◽  
Unit Of Measurement ◽  
Time Required ◽  
Graphics Processing ◽  
High Processing

This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL). This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral{P,N,Z}depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and −1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and −1 means they are perfectly inversely correlated and so is the UnifiedMetrics procedure for CPU and GPU, respectively. Another problem is the high processing time required for computing all the lexical entries in the unification task. Thus, the USL approach computes a subset of lexical entries in each of the 1344 GPU cores and uses parallel processing in order to unify 155802 lexical entries. The results of the analysis conducted using the USL approach show that the USL has 95.430 lexical entries, out of which there are 35.201 considered to be positive, 22.029 negative, and 38.200 neutral. Finally, the runtime was 10 minutes for 95.430 lexical entries; this allows a reduction of the time computing for the UnifiedMetrics by 3 times.

Music Neurotechnology for Sound Synthesis: Sound Synthesis with Spiking Neuronal Networks

Leonardo ◽  
2009 ◽  
Vol 42 (5) ◽  
pp. 439-442 ◽  
Author(s):  
Eduardo R. Miranda ◽  
John Matthias
Keyword(s):  
Cortical Neurons ◽  
Neuronal Networks ◽  
Research Area ◽  
Sound Synthesis ◽  
Ongoing Research ◽  
Computer Interfaces ◽  
Engineering Sciences ◽  
New Research ◽  
Sound Files

Music neurotechnology is a new research area emerging at the crossroads of neurobiology, engineering sciences and music. Examples of ongoing research into this new area include the development of brain-computer interfaces to control music systems and systems for automatic classification of sounds informed by the neurobiology of the human auditory apparatus. The authors introduce neurogranular sampling, a new sound synthesis technique based on spiking neuronal networks (SNN). They have implemented a neurogranular sampler using the SNN model developed by Izhikevich, which reproduces the spiking and bursting behavior of known types of cortical neurons. The neurogranular sampler works by taking short segments (or sound grains) from sound files and triggering them when any of the neurons fire.

scholarly journals Events, Time and the Simple Form

2009 ◽  
Vol 17 (1) ◽  
pp. 85-105 ◽  
Author(s):  
Walter H. Hirtle
Keyword(s):  
Simple Form ◽  
Point Of View ◽  
Event Time ◽  
The Difference ◽  
Time Required ◽  
English Verb

Abstract This is an attempt to discern more clearly the underlying or POTENTIAL meaning of the simple form of the English verb, described in Hirtle 1967 as 'perfective'. Vendler's widely accepted classification of events into ACCOMPLISHMENTS, ACHIEVEMENTS, ACTIVITIES, and STATES is examined from the point of view of the time necessarily contained between the beginning and end of any event, i.e. EVENT TIME as represented by the simple form. This examination justifies the well known dynamic/stative dichotomy by showing that event time is evoked in two different ways, that, in fact, the simple form has two ACTUAL significates. Further reflection on the difference between the two types thus expressed—developmental or action-like events and non-developmental or state-like events—leads to the conclusion that the simple form provides a representation of the time required to situate all the impressions involved in the notional or lexical import of the verb.

Value co-creation: a review of literature and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Victor Saha ◽  
Praveen Goyal ◽  
Charles Jebarajakirthy
Keyword(s):  
Systematic Review ◽  
Future Research ◽  
Review Of Literature ◽  
Relevant Research ◽  
Research Papers ◽  
Future Directions ◽  
Content Type ◽  
Extant Literature ◽  
Future Research Agenda ◽  
Theoretical Perspectives

Purpose The purpose of this paper is to present a systematic review of the available literature on value co-creation (VCC) and provide insightful future directions for research in this domain. Design/methodology/approach The extant literature on VCC has been reviewed by collecting relevant research papers based on certain specified delimiting criteria. A total of 110 research papers have been analysed to gain useful insights into VCC literature. Findings The study analyses the literature on VCC and provides a clear distinction between VCC and its closely related constructs in the literature. The study also draws significant insights from the VCC literature based on some specific parameters. Some frequently used theoretical perspectives have been discussed in the study, thus pointing towards a few alternative theories that can be used for future research. Finally, specific trends emerging from the literature have been discussed that provide a comprehensive understanding of the research inclinations of this concept, along with future scopes of research in the VCC domain. Research limitations/implications The papers were selected for this study based on some delimiting criteria. Thus, the findings cannot be generalised for the entire research on VCC. Originality/value This paper fulfils the need for a systematic review of the extant literature on VCC. The study synthesises literature and bibliography on VCC from 2004 to 2019 to benefit both academics and practitioners and gives some directions to advance this domain of literature.

Sign in / Sign up

Export Citation Format

Share Document