Deep Neural Attention for Misinformation and Deception Detection

PhD thesis in Information technologyAt present the influence of social media on society is so much that without it life seems to have no meaning for many. This kind of over-reliance on social media gives an opportunity to the anarchic elements to take undue advantage. Online misinformation and deception are vivid examples of such phenomenon. The misinformation or fake news spreads faster and wider than the true news [32]. The need of the hour is to identify and curb the spread of misinformation and misleading content automatically at the earliest. Several machine learning models have been proposed by the researchers to detect and prevent misinformation and deceptive content. However, these prior works suffer from some limitations: First, they either use feature engineering heavy methods or use intricate deep neural architectures, which are not so transparent in terms of their internal working and decision making. Second, they do not incorporate and learn the available auxiliary and latent cues and patterns, which can be very useful in forming the adequate context for the misinformation. Third, Most of the former methods perform poorly in early detection accuracy measures because of their reliance on features that are usually absent at the initial stage of news or social media posts on social networks. In this dissertation, we propose suitable deep neural attention based solutions to overcome these limitations. For instance, we propose a claim verification model, which learns embddings for the latent aspects such as author and subject of the claim and domain of the external evidence document. This enables the model to learn important additional context other than the textual content. In addition, we also propose an algorithm to extract evidential snippets out of external evidence documents, which serves as explanation of the model’s decisions. Next, we improve this model by using improved claim driven attention mechanism and also generate a topically diverse and non-redundant multi-document fact-checking summary for the claims, which helps to further interpret the model’s decision making. Subsequently, we introduce a novel method to learn influence and affinity relationships among the social media users present on the propagation paths of the news items. By modeling the complex influence relationship among the users, in addition to textual content, we learn the significant patterns pertaining to the diffusion of the news item on social network. The evaluation shows that the proposed model outperforms the other related methods in early detection performance with significant gains. Next, we propose a synthetic headline generation based headline incongruence detection model. Which uses a word-to-word mutual attention based deep semantic matching between original and synthetic news headline to detect incongruence. Further, we investigate and define a new task of incongruence detection in presence of important cardinal values in headline. For this new task, we propose a part-of-speech pattern driven attention based method, which learns requisite context for cardinal values

Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy

Modeling the Multi-mode Distribution in Self-Supervised Language Models

Self-supervised large language models (LMs) have become a highly-influential and foundational tool for many NLP models. For this reason, their expressivity is an important topic of study. In near-universal practice, given the language context, the model predicts a word from the vocabulary using a single embedded vector representation of both context and dictionary entries. Note that the context sometimes implies that the distribution over predicted words should be multi-modal in embedded space. However, the context’s single-vector representation provably fails to capture such a distribution. To address this limitation, we propose to represent context with multiple vector embeddings, which we term facets. This is distinct from previous work on multi-sense vocabulary embeddings, which employs multiple vectors for the dictionary entries, not the context. In this dissertation, we first present the theoretical limitations of the single context embedding in LMs and how the theoretical analyses suggest new alternative softmax layers that encode a context as multiple embeddings. The proposed alternatives achieve better perplexity than the mixture of softmax (MoS), especially given an ambiguous context, without adding significant computational cost to LMs. Our approaches also let GPT-2 learn to properly copy the entities from the context, which increases the coherence of the generated text without requiring any labels. In addition to predicting the next word, we also use multiple CLS embeddings to improve state-of-the-art pretraining methods for BERT on natural language understanding (NLU) benchmarks without introducing significant extra parameters or computations, especially when the training datasets are small. Furthermore, we show that our multi-facet embeddings improve the sequential recommendation, scientific paper embeddings, measurement of sentence similarity, distantly supervised relation extraction, unsupervised text pattern entailment detection, and cold-start citation recommendation. Finally, we use the multiple vector embeddings to predict the future topics of a context, and build on the basis, we propose a novel interactive language generation framework

Evolution of A Common Vector Space Approach to Multi-Modal Problems

A set of methods to address computer vision problems has been developed. Video un- derstanding is an activate area of research in recent years. If one can accurately identify salient objects in a video sequence, these components can be used in information retrieval and scene analysis. This research started with the development of a course-to-fine frame- work to extract salient objects in video sequences. Previous work on image and video frame background modeling involved methods that ranged from simple and efficient to accurate but computationally complex. It will be shown in this research that the novel approach to implement object extraction is efficient and effective that outperforms the existing state-of-the-art methods. However, the drawback to this method is the inability to deal with non-rigid motion. With the rapid development of artificial neural networks, deep learning approaches are explored as a solution to computer vision problems in general. Focusing on image and text, the image (or video frame) understanding can be achieved using CVS. With this concept, modality generation and other relevant applications such as automatic im- age description, text paraphrasing, can be explored. Specifically, video sequences can be modeled by Recurrent Neural Networks (RNN), the greater depth of the RNN leads to smaller error, but that makes the gradient in the network unstable during training.To overcome this problem, a Batch-Normalized Recurrent Highway Network (BNRHN) was developed and tested on the image captioning (image-to-text) task. In BNRHN, the highway layers are incorporated with batch normalization which diminish the gradient vanishing and exploding problem. In addition, a sentence to vector encoding framework that is suitable for advanced natural language processing is developed. This semantic text embedding makes use of the encoder-decoder model which is trained on sentence paraphrase pairs (text-to-text). With this scheme, the latent representation of the text is shown to encode sentences with common semantic information with similar vector rep- resentations. In addition to image-to-text and text-to-text, an image generation model is developed to generate image from text (text-to-image) or another image (image-to- image) based on the semantics of the content. The developed model, which refers to the Multi-Modal Vector Representation (MMVR), builds and encodes different modalities into a common vector space that achieve the goal of keeping semantics and conversion between text and image bidirectional. The concept of CVS is introduced in this research to deal with multi-modal conversion problems. In theory, this method works not only on text and image, but also can be generalized to other modalities, such as video and audio. The characteristics and performance are supported by both theoretical analysis and experimental results. Interestingly, the MMVR model is one of the many possible ways to build CVS. In the final stages of this research, a simple and straightforward framework to build CVS, which is considered as an alternative to the MMVR model, is presented

The blessings of explainable AI in operations & maintenance of wind turbines

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested