scholarly journals Comprehensive Retrospection of Literature Reported Works of Community Question Answering Systems

2020 ◽  
Vol 9 (3) ◽  
pp. 1904-1917
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Community ◽  
Question Answering ◽  
Hybrid Approach ◽  
Open Educational Resources ◽  
Stack Overflow ◽  
Community Question Answering ◽  
Question Answering Systems ◽  
Answer Quality

Community question answering CQA) systems are rapidly gaining attention in the society. Several researchers have actively engaged in improving the theories associated with question answering (QA) systems. This paper reviews the literature reported works on question answering QA systems. In this paper, we discuss on the early contributions on QA systems along with their present and future scope. We have categorized the literature reported works into 20 subgroups according to their significance and relevance. The works in each group will be brought out along with their inter-relevance. Finding the question and answer quality is the prime challenge almost addressed by many researchers. Modeling similar questions, identifying experts in prior and understanding seeker satisfaction also considered as potential challenges. Researchers at the most have done experimentations on popular CQAs like Yahoo! Answers, Wiki Answers, Baidu Knows, Brianly, Quora, Pubmed and Stack Overflow respectively. Machine learning, probabilistic modeling, deep learning and hybrid approach of solving show profound significance in addressing various challenges encounter with QA systems. Today the paradigm of CQA systems took the shift by serving as Open Educational Resources to learning community

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

2018 ◽  
Vol 18 (3-4) ◽  
pp. 623-637 ◽  
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.

scholarly journals Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series

2021 ◽  
Author(s):  
Sidra Mehtab ◽  
Jaydip Sen
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Regression Models ◽  
Research Work ◽  
Hybrid Approach ◽  
Forecast Horizon ◽  
Stock Price Prediction ◽  
Price Prediction

Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modelled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of four years: 2015 – 2018. Based on the NIFTY data during 2015 – 2018, we build various predictive models using machine learning approaches, and then use those models to predict the “Close” value of NIFTY 50 for the year 2019, with a forecast horizon of one week, i.e., five days. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual “Close” values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network (CNN) with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Hybrid approach with Deep Auto-Encoder and optimized LSTM based Deep Learning approach to detect anomaly in cloud logs

2021 ◽  
pp. 1-15
Author(s):  
Savaridassan Pankajashan ◽  
G. Maragatham ◽  
T. Kirthiga Devi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Performance Metrics ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Paper Machine ◽  
Log Data ◽  
Isolation Forest

Anomaly-based detection is coupled with recognizing the uncommon, to catch the unusual activity, and to find the strange action behind that activity. Anomaly-based detection has a wide scope of critical applications, from bank application security to regular sciences to medical systems to marketing apps. Anomaly-based detection adopted by various Machine Learning techniques is really a type of system that consists of artificial intelligence. With the ever-expanding volume and new sorts of information, for example, sensor information from an incontestably enormous amount of IoT devices and from network flow data from cloud computing, it is implicitly understood without surprise that there is a developing enthusiasm for having the option to deal with more conclusions automatically by means of AI and ML applications. But with respect to anomaly detection, many applications of the scheme are simply the passion for detection. In this paper, Machine Learning (ML) techniques, namely the SVM, Isolation forest classifiers experimented and with reference to Deep Learning (DL) techniques, the proposed DA-LSTM (Deep Auto-Encoder LSTM) model are adopted for preprocessing of log data and anomaly-based detection to get better performance measures of detection. An enhanced LSTM (long-short-term memory) model, optimizing for the suitable parameter using a genetic algorithm (GA), is utilized to recognize better the anomaly from the log data that is filtered, adopting a Deep Auto-Encoder (DA). The Deep Neural network models are utilized to change over unstructured log information to training ready features, which are reasonable for log classification in detecting anomalies. These models are assessed, utilizing two benchmark datasets, the Openstack logs, and CIDDS-001 intrusion detection OpenStack server dataset. The outcomes acquired show that the DA-LSTM model performs better than other notable ML techniques. We further investigated the performance metrics of the ML and DL models through the well-known indicator measurements, specifically, the F-measure, Accuracy, Recall, and Precision. The exploratory conclusion shows that the Isolation Forest, and Support vector machine classifiers perform roughly 81%and 79%accuracy with respect to the performance metrics measurement on the CIDDS-001 OpenStack server dataset while the proposed DA-LSTM classifier performs around 99.1%of improved accuracy than the familiar ML algorithms. Further, the DA-LSTM outcomes on the OpenStack log data-sets show better anomaly detection compared with other notable machine learning models.

scholarly journals Recent Trends in Deep Learning Based Open-Domain Textual Question Answering Systems

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 94341-94356
Author(s):  
Zhen Huang ◽  
Shiyi Xu ◽  
Minghao Hu ◽  
Xinyi Wang ◽  
Jinyan Qiu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Question Answering ◽  
Open Domain ◽  
Question Answering Systems ◽  
Recent Trends

scholarly journals BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection

2019 ◽  
Vol 33 ◽  
pp. 8102-8109 ◽  
Author(s):  
Hedi Ben-younes ◽  
Remi Cadene ◽  
Nicolas Thome ◽  
Matthieu Cord
Keyword(s):  
Deep Learning ◽  
Learning Community ◽  
Question Answering ◽  
Practical Interest ◽  
Tensor Decomposition ◽  
Representation Learning ◽  
Multimodal Fusion ◽  
Practical Implementation ◽  
Fusion Model ◽  
Visual Question Answering

Multimodal representation learning is gaining more and more interest within the deep learning community. While bilinear models provide an interesting framework to find subtle combination of modalities, their number of parameters grows quadratically with the input dimensions, making their practical implementation within classical deep learning pipelines challenging. In this paper, we introduce BLOCK, a new multimodal fusion based on the block-superdiagonal tensor decomposition. It leverages the notion of block-term ranks, which generalizes both concepts of rank and mode ranks for tensors, already used for multimodal fusion. It allows to define new ways for optimizing the tradeoff between the expressiveness and complexity of the fusion model, and is able to represent very fine interactions between modalities while maintaining powerful mono-modal representations. We demonstrate the practical interest of our fusion model by using BLOCK for two challenging tasks: Visual Question Answering (VQA) and Visual Relationship Detection (VRD), where we design end-to-end learnable architectures for representing relevant interactions between modalities. Through extensive experiments, we show that BLOCK compares favorably with respect to state-of-the-art multimodal fusion models for both VQA and VRD tasks. Our code is available at https://github.com/Cadene/block.bootstrap.pytorch.

scholarly journals Are You Talking about Software Product Lines? An Analysis of Developer Communities

2019 ◽  
Author(s):  
Jacob Krüger
Keyword(s):  
Question Answering ◽  
Practical Knowledge ◽  
Software Product Lines ◽  
Product Line ◽  
Product Lines ◽  
Systematic Analysis ◽  
Technical Problems ◽  
Community Question Answering ◽  
Software Product ◽  
Question Answering Systems

Community-question-answering systems, such as Stack Overflow, provide a platform for various communities to ask questions, dis- cuss topics, and find knowledge. Especially software developers are heavily relying on such systems to identify solutions for their problems. While the content of community-question-answering systems may be less scientific, it usually represents practical knowledge from various perspectives and backgrounds. Thus, analyzing this content can be valuable for the scientific community to understand previous and current (i.e., open questions) needs of practitioners. In this paper, we report a systematic analysis of two websites that comprise communities with a focus on software development: Stack Exchange and Quora. We extract questions, answers, comments, and discussions on software product lines in general and feature modeling in particular. The results provide a historical perspective, an overview on commonly addressed scopes, and a classification of discussed topics and problems. Moreover, our findings are interesting to understand the practical impact of software-product-line techniques outside of well-analyzed case studies, to support lectures by identifying regularly asked questions, and to scope tool development based on reported technical problems.

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