Mutual Information Decay Curves and Hyper-Parameter Grid Search Design for Recurrent Neural Architectures

We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.Comment: Published at the 27th International Conference on Neural Information Processing, ICONIP 2020, Bangkok, Thailand, November 18-22, 2020. arXiv admin note: text overlap with arXiv:1810.0296

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Generating synthetic mixed-type longitudinal electronic health records for artificial intelligent applications

The recent availability of electronic health records (EHRs) have provided enormous opportunities to develop artificial intelligence (AI) algorithms. However, patient privacy has become a major concern that limits data sharing across hospital settings and subsequently hinders the advances in AI. Synthetic data, which benefits from the development and proliferation of generative models, has served as a promising substitute for real patient EHR data. However, the current generative models are limited as they only generate single type of clinical data for a synthetic patient, i.e., either continuous-valued or discrete-valued. To mimic the nature of clinical decision-making which encompasses various data types/sources, in this study, we propose a generative adversarial network (GAN) entitled EHR-M-GAN that simultaneously synthesizes mixed-type timeseries EHR data. EHR-M-GAN is capable of capturing the multidimensional, heterogeneous, and correlated temporal dynamics in patient trajectories. We have validated EHR-M-GAN on three publicly-available intensive care unit databases with records from a total of 141,488 unique patients, and performed privacy risk evaluation of the proposed model. EHR-M-GAN has demonstrated its superiority over state-of-the-art benchmarks for synthesizing clinical timeseries with high fidelity, while addressing the limitations regarding data types and dimensionality in the current generative models. Notably, prediction models for outcomes of intensive care performed significantly better when training data was augmented with the addition of EHR-M-GAN-generated timeseries. EHR-M-GAN may have use in developing AI algorithms in resource-limited settings, lowering the barrier for data acquisition while preserving patient privacy

A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Datasets, Clues and State-of-the-Arts for Multimedia Forensics: An Extensive Review

With the large chunks of social media data being created daily and the parallel rise of realistic multimedia tampering methods, detecting and localising tampering in images and videos has become essential. This survey focusses on approaches for tampering detection in multimedia data using deep learning models. Specifically, it presents a detailed analysis of benchmark datasets for malicious manipulation detection that are publicly available. It also offers a comprehensive list of tampering clues and commonly used deep learning architectures. Next, it discusses the current state-of-the-art tampering detection methods, categorizing them into meaningful types such as deepfake detection methods, splice tampering detection methods, copy-move tampering detection methods, etc. and discussing their strengths and weaknesses. Top results achieved on benchmark datasets, comparison of deep learning approaches against traditional methods and critical insights from the recent tampering detection methods are also discussed. Lastly, the research gaps, future direction and conclusion are discussed to provide an in-depth understanding of the tampering detection research arena

Synthesizing Cyber Intrusion Alerts using Generative Adversarial Networks

Cyber attacks infiltrating enterprise computer networks continue to grow in number, severity, and complexity as our reliance on such networks grows. Despite this, proactive cyber security remains an open challenge as cyber alert data is often not available for study. Furthermore, the data that is available is stochastically distributed, imbalanced, lacks homogeneity, and relies on complex interactions with latent aspects of the network structure. Currently, there is no commonly accepted way to model and generate synthetic alert data for further study; there are also no metrics to quantify the fidelity of synthetically generated alerts or identify critical attributes within the data. This work proposes solutions to both the modeling of cyber alerts and how to score the fidelity of such models. Generative Adversarial Networks are employed to generate cyber alert data taken from two collegiate penetration testing competitions. A list of criteria defining desirable attributes for cyber alert data metrics is provided. Several statistical and information-theoretic metrics, such as histogram intersection and conditional entropy, meet these criteria and are used for analysis. Using these metrics, critical relationships of synthetically generated alerts may be identified and compared to data from the ground truth distribution. Finally, through these metrics, we show that adding a mutual information constraint to the model’s generation increases the quality of outputs and successfully captures alerts that occur with low probability

Can Tabular Generative Models Generate Realistic Synthetic Near Infrared Spectroscopic Data?

In this thesis, we evaluated the performance of two generative models, Conditional Tabular Gen- erative Adversarial Network (CTGAN) and Tabular Variational Autoencoder (TVAE), from the open-source library Synthetic Data Vault (SDV), for generating synthetic Near Infrared (NIR) spectral data. The aim was to assess the viability of these models in synthetic data generation for predicting Dry Matter Content (DMC) in the field of NIR spectroscopy. The fidelity and utility of the synthetic data were examined through a series of benchmarks, including statistical comparisons, dimensionality reduction, and machine learning tasks. The results showed that while both CTGAN and TVAE could generate synthetic data with statistical properties similar to real data, TVAE outperformed CTGAN in terms of preserving the correlation structure of the data and the relationship between the features and the target variable, DMC. However, the synthetic data fell short in fooling machine learning classifiers, indicating a persisting challenge in synthetic data generation. With respect to utility, neither synthetic dataset produced by CTGAN or TVAE could serve as a satisfactory substitute for real data in training machine learning models for predicting DMC. Although TVAE-generated synthetic data showed some potential when used with Random For- est (RF) and K-Nearest Neighbors (KNN) classifiers, the performance was still inadequate for practical use. This study offers valuable insights into the use of generative models for synthetic NIR spectral data generation, highlighting their current limitations and potential areas for future research