Deep Cross-Modal Correlation Learning for Audio and Lyrics in Music Retrieval

Deep cross-modal learning has successfully demonstrated excellent performance in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, little research focuses on cross-modal correlation learning where temporal structures of different data modalities such as audio and lyrics should be taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Data in different modalities are converted to the same canonical space where inter modal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study that uses deep architectures for learning the temporal correlation between audio and lyrics. A pre-trained Doc2Vec model followed by fully-connected layers is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: i) We propose an end-to-end network to learn cross-modal correlation between audio and lyrics, where feature extraction and correlation learning are simultaneously performed and joint representation is learned by considering temporal structures. ii) As for feature extraction, we further represent an audio signal by a short sequence of local summaries (VGG16 features) and apply a recurrent neural network to compute a compact feature that better learns temporal structures of music audio. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval

Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications

With the abundant amount of available online and offline text data, there arises a crucial need to extract the relation between phrases and summarize the main content of each document in a few words. For this purpose, there have been many studies recently in Open Information Extraction (OIE). OIE improves upon relation extraction techniques by analyzing relations across different domains and avoids requiring hand-labeling pre-specified relations in sentences. This paper surveys recent approaches of OIE and its applications on Knowledge Graph (KG), text summarization, and Question Answering (QA). Moreover, the paper describes OIE basis methods in relation extraction. It briefly discusses the main approaches and the pros and cons of each method. Finally, it gives an overview about challenges, open issues, and future work opportunities for OIE, relation extraction, and OIE applications.Comment: 15 pages, 9 figure

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Software similarity in different programming codes is a rapidly evolving field because of its numerous applications in software development, software cloning, software plagiarism, and software forensics. Currently, software researchers and developers search cross-language open-source repositories for similar applications for a variety of reasons, such as reusing programming code, analyzing different implementations, and looking for a better application. However, it is a challenging task because each programming language has a unique syntax and semantic structure. In this paper, a novel tool called Cross-Language Software Similarity (CroLSSim) is designed to detect similar software applications written in different programming codes. First, the Abstract Syntax Tree (AST) features are collected from different programming codes. These are high-quality features that can show the abstract view of each program. Then, Methods Description (MDrep) in combination with AST is used to examine the relationship among different method calls. Second, the Term Frequency Inverse Document Frequency approach is used to retrieve the local and global weights from AST-MDrep features. Third, the Latent Semantic Analysis-based features extraction and selection method is proposed to extract the semantic anchors in reduced dimensional space. Fourth, the Convolution Neural Network (CNN)-based features extraction method is proposed to mine the deep features. Finally, a hybrid deep learning model of CNN-Long-Short-Term Memory is designed to detect semantically similar software applications from these latent variables. The data set contains approximately 9.5K Java, 8.8K C#, and 7.4K C++ software applications obtained from GitHub. The proposed approach outperforms as compared with the state-of-the-art methods

Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models

Deep Learning para BigData

We live in a world where data is becoming increasingly valuable and increasingly abundant in volume. Every company produces data, be it from sales, sensors, and various other sources. Since the dawn of the smartphone, virtually every person in the world is connected to the internet and contributes to data generation. Social networks are big contributors to this Big Data boom. How do we extract insight from such a rich data environment? Is Deep Learning capable of circumventing Big Data’s challenges? This is what we intend to understand. To reach a conclusion, Social Network data is used as a case study for predicting sentiment changes in the Stock Market. The objective of this dissertation is to develop a computational study and analyse its performance. The outputs will contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment analysis.Vivemos num mundo onde dados são cada vez mais valiosos e abundantes. Todas as empresas produzem dados, sejam eles provenientes de valores de vendas, parâmetros de sensores bem como de outras diversas fontes. Desde que os smartphones se tornaram pessoais, o mundo tornou-se mais conectado, já que virtualmente todas as pessoas passaram a ter a internet na ponta dos dedos. Esta explosão tecnológica foi acompanhada por uma explosão de dados. As redes sociais têm um grande contributo para a quantidade de dados produzida. Mas como se analisam estes dados? Será que Deep Learning poderá dar a volta aos desafios que Big Data traz inerentemente? É isso se pretende perceber. Para chegar a uma conclusão, foi utilizado um caso de estudo de redes sociais para previsão de alterações nas ações de mercados financeiros relacionadas com as opiniões dos utilizadores destas. O objetivo desta dissertação é o desenvolvimento de um estudo computacional e a análise da sua performance. Os resultados contribuirão para entender o uso de Deep Learning com Big Data, com especial foco em análise de sentimento. The objective of this dissertation is to develop a computational study and analyse its performance. The outputs will contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment analysis

Automatically generated summaries of sports videos based on semantic content

The sport has been a part of our lives since the beginning of times, whether we are spectators or participants. The diffusion and increase of multimedia platforms made the consumption of these contents available to everyone. Sports videos appeal to a large population all around the world and have become an important form of multimedia content that is streamed over the Internet and television networks. Moreover, sport content creators want to provide the users with relevant information such as live commentary, summarization of the games in form of text or video using automatic tools.As a result, MOG-Technologies wants to create a tool capable of summarizing football matches based on semantic content, and this problem was explored in the scope of this Dissertation. The main objective is to convert the television football commentator's speech into text taking advantage of Google's Speech-to-Text tool. Several machine learning models were then tested to classify sentences into important events. For the model training, a dataset was created, combining 43 games transcription from different television channels also from 72 games provided by Google Search timeline commentary, the combined dataset contains 3260 sentences. To validate the proposed solution the accuracy and f1 score were extracted for each machine learning model.The results show that the developed tool is capable of predicting events in live events, with low error rate. Also, combining multiple sources, not only the sport commentator speech, will help to increase the performance of the tool. It is important to notice that the dataset created during this Dissertation will allow MOG-Technologies to expand and perfect the concept discussed in this project