A content spectral-based text representation

In the last few years, text analysis has grown as a keystone in several domains for solving many real-world problems, such as machine translation, spam detection, and question answering, to mention a few. Many of these tasks can be approached by means of machine learning algorithms. Most of these algorithms take as input a transformation of the text in the form of feature vectors containing an abstraction of the content. Most of recent vector representations focus on the semantic component of text, however, we consider that also taking into account the lexical and syntactic components the abstraction of content could be beneficial for learning tasks. In this work, we propose a content spectral-based text representation applicable to machine learning algorithms for text analysis. This representation integrates the spectra from the lexical, syntactic, and semantic components of text producing an abstract image, which can also be treated by both, text and image learning algorithms. These components came from feature vectors of text. For demonstrating the goodness of our proposal, this was tested on text classification and complexity reading score prediction tasks obtaining promising results.

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Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples

Theory and Practice of Logic Programming ◽

10.1017/s1471068418000248 ◽

2018 ◽

Vol 18 (3-4) ◽

pp. 623-637 ◽

Cited By ~ 2

Author(s):

ARINDAM MITRA ◽

CHITTA BARAL

Keyword(s):

Machine Learning ◽

Learning Community ◽

Question Answering ◽

Learning Algorithms ◽

Inductive Logic ◽

Opportunity To Learn ◽

Machine Learning Algorithms ◽

Knowledge Representation And Reasoning ◽

Handwritten Digit ◽

Answer Set

AbstractOver the years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn answer set programs. We present a sound and complete algorithm which takes the input in a slightly different manner and performs an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available athttps://goo.gl/KdWAcV.

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Toward Palmprint Recognition Methodology Based Machine Learning Techniques

European Journal of Electrical Engineering and Computer Science ◽

10.24018/ejece.2020.4.4.225 ◽

2020 ◽

Vol 4 (4) ◽

Author(s):

M. M. Ata ◽

K. M. Elgamily ◽

M. A. Mohamed

Keyword(s):

Machine Learning ◽

Hough Transform ◽

Recognition Accuracy ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Palmprint Recognition ◽

Feature Vectors ◽

Learning Techniques

The presented paper proposes an algorithm for palmprint recognition using seven different machine learning algorithms. First of all, we have proposed a region of interest (ROI) extraction methodology which is a two key points technique. Secondly, we have performed some image enhancement techniques such as edge detection and morphological operations in order to make the ROI image more suitable for the Hough transform. In addition, we have applied the Hough transform in order to extract all the possible principle lines on the ROI images. We have extracted the most salient morphological features of those lines; slope and length. Furthermore, we have applied the invariant moments algorithm in order to produce 7 appropriate hues of interest. Finally, after performing a complete hybrid feature vectors, we have applied different machine learning algorithms in order to recognize palmprints effectively. Recognition accuracy have been tested by calculating precision, sensitivity, specificity, accuracy, dice, Jaccard coefficients, correlation coefficients, and training time. Seven different supervised machine learning algorithms have been implemented and utilized. The effect of forming the proposed hybrid feature vectors between Hough transform and Invariant moment have been utilized and tested. Experimental results show that the feed forward neural network with back propagation has achieved about 99.99% recognition accuracy among all tested machine learning techniques.

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CoRg: Commonsense Reasoning Using a Theorem Prover and Machine Learning

10.29007/lt5p ◽

2019 ◽

Cited By ~ 1

Author(s):

Sophie Siebert ◽

Frieder Stolzenburg

Keyword(s):

Machine Learning ◽

Question Answering ◽

Learning Algorithms ◽

Knowledge Bases ◽

Black Box ◽

Machine Learning Algorithms ◽

Theorem Prover ◽

Commonsense Reasoning ◽

Probable Answer ◽

Everyday Task

Commonsense reasoning is an everyday task that is intuitive for humans but hard to implement for computers. It requires large knowledge bases to get the required data from, although this data is still incomplete or even inconsistent. While machine learning algorithms perform rather well on these tasks, the reasoning process remains a black box. To close this gap, our system CoRg aims to build an explainable and well-performing system, which consists of both an explainable deductive derivation process and a machine learning part. We conduct our experiments on the Copa question-answering benchmark using the ontologies WordNet, Adimen-SUMO, and ConceptNet. The knowledge is fed into the theorem prover Hyper and in the end the conducted models will be analyzed using machine learning algorithms, to derive the most probable answer.

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Machine Learning: A Quantum Perspective

10.3233/apc210214 ◽

2021 ◽

Author(s):

Aishwarya Jhanwar ◽

Manisha J. Nene

Keyword(s):

Machine Learning ◽

Quantum Mechanics ◽

Quantum Computing ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Quantum Computers ◽

Learning Tasks ◽

Current Scenario ◽

Chip Fabrication ◽

Classical Computing

Recently, increased availability of the data has led to advances in the field of machine learning. Despite of the growth in the domain of machine learning, the proximity to the physical limits of chip fabrication in classical computing is motivating researchers to explore the properties of quantum computing. Since quantum computers leverages the properties of quantum mechanics, it carries the ability to surpass classical computers in machine learning tasks. The study in this paper contributes in enabling researchers to understand how quantum computers can bring a paradigm shift in the field of machine learning. This paper addresses the concepts of quantum computing which influences machine learning in a quantum world. It also states the speedup observed in different machine learning algorithms when executed on quantum computers. The paper towards the end advocates the use of quantum application software and throw light on the existing challenges faced by quantum computers in the current scenario.

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POSTER| Address2vec: Generating Vector Embeddings for Blockchain Analytics

10.31730/osf.io/9pcmn ◽

2019 ◽

Author(s):

Ali Hussein ◽

Samiiha Nalwooga

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Current Work ◽

Main Method ◽

Recent Growth ◽

Crypto Currency ◽

Generate Vector ◽

Vector Representations ◽

Random Approach

Bitcoin Blockchain is a completely public open currency transaction ledger, recent growth in the crypto-currency market has driven many lawful and blackmarket actors using Bitcoin as the main method of payment. I propose address2vec an algorithm to generate vector representations of addresses on the Bitcoin Blockchain.I am able to present better results than a baseline random approach in predicting hoarding vs spending beaviour. The current work allows for utilization of common machine learning algorithms on bitcoin transaction addresses.

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Comparative Study of Machine Learning Algorithms for Social Media Text Analysis

Data Science and Analytics - Communications in Computer and Information Science ◽

10.1007/978-981-15-5830-6_19 ◽

2020 ◽

pp. 223-235

Author(s):

Nidhi Malik ◽

Saksham Jain

Keyword(s):

Machine Learning ◽

Social Media ◽

Comparative Study ◽

Text Analysis ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Social Media Text

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Predicting crystallisation propensity of small molecules

Acta Crystallographica Section A Foundations and Advances ◽

10.1107/s2053273314083715 ◽

2014 ◽

Vol 70 (a1) ◽

pp. C1628-C1628 ◽

Cited By ~ 1

Author(s):

Jerome Wicker ◽

Richard Cooper ◽

William David

Keyword(s):

Machine Learning ◽

Predictive Accuracy ◽

Learning Algorithms ◽

Amino Acid Sequences ◽

Machine Learning Algorithms ◽

Training Data ◽

Learning Tasks ◽

Zinc Database ◽

Training Examples

We show that suitably chosen machine learning algorithms can be used to predict the "crystallisation propensity" of classes of molecules with a promisingly low error rate, using the Cambridge Structural Database and ZINC database to provide training examples of crystalline and non-crystalline molecules. Supervised learning tasks involve using machine learning algorithms to infer a function from known training data which allows classification of unknown test data. Such algorithms have been successfully used to predict continuous properties of compounds, such as melting point[1] and solubility[2]. Similar methods have also been applied to protein crystallinity predictions based on amino acid sequences[3], but little has previously been done to attempt to classify small organic molecules as crystalline or non-crystalline due to the difficulty in finding descriptors appropriate to the problem. Our approach uses only information about the atomic types and connectivity, leaving aside the confounding effects of solvents and crystallisation conditions. The result is reinforced by a blind microcrystallisation screening of a sample of materials, which confirmed the classification accuracy of the predictive model. An analysis of the most significant descriptors used in the classification is also presented, and we show that significant predictive accuracy can be obtained using relatively few descriptors.

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