Towards Synthetic and Balanced Digital Government Benchmarking

Reliable benchmarking is essential for effective management of the government digitalization efforts. Existing benchmarking instruments generally fail to support this target. One problem is the diversity of instruments, resulting in a split image of digital progress and adding ambiguity to policy decisions. Another problem is disconnect in assessing progress between digital and traditional “analog” governance, lending support to a dangerous idea that countries can compensate for lack of progress in their governance systems by simply digitalizing them. This paper provides a path to addressing both problems by: aggregating relevant indicators of the World Economic Forum’s Network Readiness Index (NRI) to obtain a single synthetic measure of digital government, balancing this measure with progress in analog governance using World Bank’s Worldwide Governance Indicators (WGI), calculating new measures for the latest editions of NRI and WGI, and discussing results. Technically, the paper applies multidimensional linear ordering and factor analysis

Neural Approximate Sufficient Statistics for Implicit Models

We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractable, but sampling data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mutual information maximizing representations of the data with the help of deep neural networks. The infomax learning procedure does not need to estimate any density or density ratio. We apply our approach to both traditional approximate Bayesian computation and recent neural likelihood methods, boosting their performance on a range of tasks.Comment: ICLR2021 spotligh

MONITORING DATA AGGREGATION OF DYNAMIC SYSTEMS USING INFORMATION TECHNOLOGIES

The subject matter of the article is models, methods and information technologies of monitoring data aggregation. The goal of the article is to determine the best deep learning model for reducing the dimensionality of dynamic systems monitoring data. The following tasks were solved: analysis of existing dimensionality reduction approaches, description of the general architecture of vanilla and variational autoencoders, development of their architecture, development of software for training and testing of autoencoders, conducting research on the performance quality of autoencoders for the problem of dimensionality reduction. The following models and methods were used: data processing and preparation, data dimensionality reduction. The software was developed using the Python language. Scikit-learn, Pandas, PyTorch, NumPy, argparse and others were used as auxiliary libraries. Obtained results: the work presents a classification of models and methods for dimensionality reduction, general reviews of vanilla and variational autoencoders, which include a description of the models, their properties, loss functions and their application to the problem of dimensionality reduction. Custom autoencoder architectures were also created, including visual representations of the autoencoder architecture and descriptions of each component. The software for training and testing autoencoders was developed, the dynamic system monitoring data set, and the steps for pre-training the data set were described. The metric for evaluating the quality of models is also described; the configuration of autoencoders and their training are considered. Conclusions: The vanilla autoencoder recovers the data much better than the variational one. Looking at the fact that the architectures of the autoencoders are the same, except for the peculiarities of the autoencoders, it can be noted that a vanilla autoencoder compresses data better by keeping more useful variables for later recovery from the bottleneck. Additionally, by training on different bottleneck sizes, you can determine the size at which the data is recovered best, which means that the most important variables are preserved. Looking at the results in general, the autoencoders work effectively for the dimensionality reduction task and the data recovery quality metric shows that they recover the data well with an error of 3–4 digits after 0. In conclusion, the vanilla autoencoder is the best deep learning model for aggregating monitoring data of dynamic systems

Machine Autonomy : Definition, Approaches, Challenges and Research Gaps

Postprin

An explainable AI-based fault diagnosis model for bearings.

In this paper, an explainable AI-based fault diagnosis model for bearings is proposed with five stages, i.e., (1) a data preprocessing method based on the Stockwell Transformation Coefficient (STC) is proposed to analyze the vibration signals for variable speed and load conditions, (2) a statistical feature extraction method is introduced to capture the significance from the invariant pattern of the analyzed data by STC, (3) an explainable feature selection process is proposed by introducing a wrapper-based feature selector—Boruta, (4) a feature filtration method is considered on the top of the feature selector to avoid the multicollinearity problem, and finally, (5) an additive Shapley ex-planation followed by k-NN is proposed to diagnose and to explain the individual decision of the k-NN classifier for debugging the performance of the diagnosis model. Thus, the idea of explaina-bility is introduced for the first time in the field of bearing fault diagnosis in two steps: (a) incorpo-rating explainability to the feature selection process, and (b) interpretation of the classifier performance with respect to the selected features. The effectiveness of the proposed model is demon-strated on two different datasets obtained from separate bearing testbeds. Lastly, an assessment of several state-of-the-art fault diagnosis algorithms in rotating machinery is included

Explainable fault prediction using learning fuzzy cognitive maps

IoT sensors capture different aspects of the environment and generate high throughput data streams. Besides capturing these data streams and reporting the monitoring information, there is significant potential for adopting deep learning to identify valuable insights for predictive preventive maintenance. One specific class of applications involves using Long Short-Term Memory Networks (LSTMs) to predict faults happening in the near future. However, despite their remarkable performance, LSTMs can be very opaque. This paper deals with this issue by applying Learning Fuzzy Cognitive Maps (LFCMs) for developing simplified auxiliary models that can provide greater transparency. An LSTM model for predicting faults of industrial bearings based on readings from vibration sensors is developed to evaluate the idea. An LFCM is then used to imitate the performance of the baseline LSTM model. Through static and dynamic analyses, we demonstrate that LFCM can highlight (i) which members in a sequence of readings contribute to the prediction result and (ii) which values could be controlled to prevent possible faults. Moreover, we compare LFCM with state-of-the-art methods reported in the literature, including decision trees and SHAP values. The experiments show that LFCM offers some advantages over these methods. Moreover, LFCM, by conducting a what-if analysis, could provide more information about the black-box model. To the best of our knowledge, this is the first time LFCMs have been used to simplify a deep learning model to offer greater explainability

Developing novel quantitative imaging analysis schemes based machine learning for cancer research

The computer-aided detection (CAD) scheme is a developing technology in the medical imaging field, and it attracted extensive research interest in recent years. In this dissertation, I investigated the feasibility of developing several new novel CAD schemes for different cancer research purposes. First, I investigated the feasibility of identifying a new quantitative imaging marker based on false-positives generated by a computer-aided detection (CAD) scheme to predict short-term breast cancer risk. For this study, an existing CAD scheme was applied “as is” to process each image. From CAD-generated results, some detection features were computed from each image. Two logistic regression models were then trained and tested using a leave-one-case-out cross-validation method to predict each testing case's likelihood of being positive in the next subsequent screening. This study demonstrated that CAD-generated false-positives contain valuable information to predict short-term breast cancer risk. Second, I identified and applied quantitative imaging features computed from ultrasound images of athymic nude mice to predict tumor response to treatment at an early stage. For this study, a CAD scheme was developed to perform tumor segmentation and image feature analysis. The study demonstrated the feasibility of extracting quantitative image features from the ultrasound images taken at an early treatment stage to predict tumor response to therapies. Last, I optimized a machine learning model for predicting peritoneal metastasis in gastric cancer. For this purpose, I have developed a CAD scheme to segment the tumor volume and extract quantitative image features automatically. Then, I reduced the dimensionality of features with a new method named random projection to optimize the model's performance. Finally, the gradient boosting machine model was applied along with a synthetic minority oversampling technique to predict peritoneal metastasis risk. Results suggested that the random projection method yielded promising results in improving the accuracy performance in peritoneal metastasis prediction. In summary, in my Ph.D. studies, I have investigated and tested several innovative approaches to develop different CAD schemes and identify quantitative imaging markers with high discriminatory power in various cancer research applications. Study results demonstrated the feasibility of applying CAD technology to several new application fields, which can help radiologists and gynecologists improve accuracy and consistency in disease diagnosis and prognosis assessment of using the medical image

Machine learning for the genetic prediction of Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia in humans, with disease course involving initial memory loss, a subsequent debilitative state and eventually death. It is a polygenic disorder, meaning its genetic component comprises many known and unknown mutations. This complexity alongside further influences from a range of lifestyle factors, have made the prediction of disease risk a challenging pursuit. The initial attempts to predict AD risk from genetic data arose due to the identification of risk loci in genome wide association studies (GWAS). Resulting variants are used to assess risk of disease onset through polygenic risk scoring (PRS). This score is generated through the summation of risk alleles multiplied by their respective effect sizes derived from GWAS. Publication results demonstrate PRS to be a useful method for assessing lifetime risk, however it has also been proven that PRS can only cover a fraction of genetic liability for AD. A possible explanation for this inadequacy is the inability for PRS to assess non-linear relationships between loci due to the use of linear modelling. Given AD is a complex polygenic disorder, it is likely that onset is the result of interactions between loci. A format which holds the capability to analyse non-linear patterns is machine learning (ML). Interest in these algorithms has increased in recent decades due to their predictive power, ability to analyse large datasets, and capabilities in disease prediction. Initial results demonstrated a superior performance for PRS compared to ML when using datasets comprising smalls amount of AD associated single nucleotide polymorphisms (SNPs). However, in some instances ML achieved accuracies close to that of PRS. This occurred when using the algorithm support vector machine with various kernels. However, it was acknowledged these algorithms would result in excessive training times when using larger datasets in subsequent chapters. Therefore, only decision tree-based algorithms were employed moving forwards. It was also deduced that techniques such as balancing by age and sex had made no discernible difference on model performance. Further investigation involved the use of variants sourced on a genome wide scale, as it was reasoned that using a greater number of SNPs might improve upon results from the previous 4 chapter. However, increasing the number of variants resulted in issues relating to high dimensionality. Despite efforts to alleviate this through the use of feature selection techniques, prediction performance for ML models was still inferior to PRS. Further avenues were also explored such as using a more lenient threshold of r2 when clumping and removing this step completely for SNP selection, but this again failed to improve upon ML prediction accuracy. PRS continued to achieve better performance when using an imputed version of the dataset used in previous analyses, this was still evident when again exploring method such as feature selection. However, the observed difference between ML and PRS was reduced in the final investigations conducted in this thesis. Analysis on datasets comprising SNPs derived from biologically associated AD pathways resulted in improved ML performance. This result identified the possibility of focusing on the underpinning biological mechanisms of AD when selecting datasets