Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

PREDICTING COLLECTIVE VIOLENCE FROM COORDINATED HOSTILE INFORMATION CAMPAIGNS IN SOCIAL MEDIA

The ability to predict conflicts prior to their occurrence can help deter the outbreak of collective violence and avoid human suffering. Existing approaches use statistical and machine learning models, and even social network analysis techniques; however, they are generally confined to long-range predictions in specific regions and are based on only a few languages. Understanding collective violence from signals in multiple or mixed languages in social media remains understudied. In this work, we construct a multilingual language model (MLLM) that can accept input from any language in social media, a model that is language-agnostic in nature. The purpose of this study is twofold. First, it aims to collect a multilingual violence corpus from archived Twitter data using a proposed set of heuristics that account for spatial-temporal features around past and future violent events. And second, it attempts to compare the performance of traditional machine learning classifiers against deep learning MLLMs for predicting message classes linked to past and future occurrences of violent events. Our findings suggest that MLLMs substantially outperform traditional ML models in predictive accuracy. One major contribution of our work is that military commands now have a tool to evaluate and learn the language of violence across all human languages. Finally, we made the data, code, and models publicly available.Outstanding ThesisCommander, Ecuadorian NavyApproved for public release. Distribution is unlimited

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Data discovery through profile-based similarity metrics

The most essential step in a data integration process is to find the datasets whose combined information provides relevant insights. This task, defined as data discovery, is highly dependent on the definition of the similarity between the candidate attributes to join, which commonly involves assessing the closeness of the semantic concepts that the two attributes represent. Most of the state-of-the-art approaches to this issue rely on syntactic methodologies, that is, procedures in which the instances of the two columns are compared to determine whether they are similar or not. These approaches suffice when the two sets of instances share the same syntactic representation but fail to detect cases in which the same semantic idea is represented by different sets of values. This latter case is ever-increasing in proportion, given the characteristics of big-data environments and the lack of standardization of the data. The aim of this project is to develop a system that can solve this issue and facilitate the establishment of relationships between related data that do not share a syntactic relationship. The approach presented in this work leverages the extensively studied syntactic methodologies to data discovery in conjunction with a new formulation for semantic similarity: the resemblance of probability distributions. Additionally, this system will be made scalable and able to handle vast quantities of data

Automatic detection of persuasion attempts on social networks

The rise of social networks and the increasing amount of time people spend on them have created a perfect place for the dissemination of false narratives, propaganda, and manipulated content. In order to prevent the spread of disinformation, content moderation is needed, however it is unfeasible to do it manually due to the large number of daily posts. This dissertation aims at solving this problem by creating a system for automatic detection of persuasion techniques, as proposed in a SemEval challenge. We start by reviewing classic machine learning and natural language processing approaches and go through more sophisticated deep learning approaches which are more suited for this type of complex problem. The classic machine learning approaches are used to create a baseline for the problem. The architecture proposed, using deep learning techniques, is built on top of a DistilBERT transformer followed by Convolutional Neural Networks. We study how our usage of different loss functions, pre-processing the text, freezing DistilBERT layers and performing hyperparameter search impact the performance of our system. We discovered that we could optimize our architecture by freezing the two initial DistilBERT’s layers and using asymmetric loss to tackle the class imbalance on the dataset presented. This study resulted in three final models with the same architecture but using different parameters where the first showed signs of overfitting, one did not show sings of overfitting but did not seem to converge and other seemed to converge but yielded the worst performance of all three. They presented a micro f1-score of 0.551, 0.526 and 0.509 and were placed in 3rd, 6th and 11th place respectively in the overall table. The models can only classify textual elements as the multimodal component is not implemented on this iteration but only discussed; Sumário: Deteção automática de tentativas de persuasão em redes sociais - O crescimento das redes sociais e o aumento do tempo que as pessoas passam nelas criaram um lugar perfeito para a disseminação de falsas narrativas, propaganda e conteúdo manipulado. Para evitar a disseminação da desinformação, é necessária a moderação do conteúdo, porém é inviável fazê-la manualmente devido ao grande número de conteúdo diário. Esta dissertação visa resolver este problema através da criação de um sistema de deteção automática de técnicas de persuasão, conforme proposto num desafio da SemEval. Começamos por rever as abordagens clássicas de aprendizagem automática e processamento de linguagem natural, passamos de seguida por abordagens mais sofisticadas de aprendizagem profunda que são mais adequadas para esse tipo de problema complexo. As abordagens clássicas de aprendizagem automática são usadas para criar um ponto de partida para o problema. A arquitetura proposta, utilizando técnicas de aprendizagem profunda, é construída sobre um transformer DistilBERT seguido de redes neuronais convolucionais. Estudamos de que forma o uso de diferentes funções ativação, pré-processamento do texto, congelamento de camadas do DistilBERT e realização de pesquisa de hiperparâmetros afetam o desempenho do nosso sistema. Descobrimos que poderíamos otimizar nossa arquitetura congelando as duas camadas iniciais do DistilBERT e usando asymmetric loss para lidar com o desequilíbrio de classes no conjunto de dados apresentado. Este estudo resultou em três modelos finais com a mesma arquitetura, mas usando parâmetros diferentes, onde o primeiro mostrou sinais de overfitting, um não mostrou sinais de overfitting mas não parece convergir e outro parece convergir, mas produziu o pior desempenho de todos os três. Apresentaram micro f1-score de 0.551, 0.526 e 0.509 e ficaram em 3º, 6º e 11º lugares, respectivamente, na tabela geral. Os modelos podem apenas classificar elementos textuais, pois o componente multimodal não é implementado nesta iteração, mas apenas discutido