Complex systems optimization is a critical challenge in real production and also the hot spot of academic research. The key factors that raise systems' complexity include (but are not limited to): inestimable structures, computationally intensive evaluations, stochastic noise, and multiple key performance indicators (KPIs). A typical example is a simulation-based optimization for an emergency department. Suppose we aim to optimize the patients' flow cost and departments' closeness by determining the corridors' widths via a simulation model. Due to the characteristics of the simulation model, there exists no explicit expression of the input and output, and the estimations are time-consuming and noise-corrupted. Furthermore, the multilevel performance indicators also lay a burden on optimization problems.

Interactive Educational Systems (IES) enabled researchers to trace student knowledge in different skills and provide recommendations for a better learning path. To estimate the student knowledge and further predict their future performance, the interest in utilizing the student interaction data captured by IES to develop learner performance models is increasing rapidly. Moreover, with the advances in computing systems, the amount of data captured by these IES systems is also increasing that enables deep learning models to compete with traditional logistic models and Markov processes. However, it is still not empirically evident if these deep models outperform traditional models on the current scale of datasets with millions of student interactions. In this work, we adopt EdNet, the largest student interaction dataset publicly available in the education domain, to understand how accurately both deep and traditional models predict future student performances. Our work observes that logistic regression models with carefully engineered features outperformed deep models from extensive experimentation. We follow this analysis with interpretation studies based on Locally Interpretable Model-agnostic Explanation (LIME) to understand the impact of various features on best performing model pre-dictions.

We investigate the self-attention mechanism of BERT in a fine-tuning scenario for the classification of scientific articles over a taxonomy of research disciplines. We observe how self-attention focuses on words that are highly related to the domain of the article. Particularly, a small subset of vocabulary words tends to receive most of the attention. We compare and evaluate the subset of the most attended words with feature selection methods normally used for text classification in order to characterize self-attention as a possible feature selection approach. Using ConceptNet as ground truth, we also find that attended words are more related to the research fields of the articles. However, conventional feature selection methods are still a better option to learn classifiers from scratch. This result suggests that, while self-attention identifies domain-relevant terms, the discriminatory information in BERT is encoded in the contextualized outputs and the classification layer. It also raises the question whether injecting feature selection methods in the self-attention mechanism could further optimize single sequence classification using transformers.

Network embedding maps the nodes of a given network into a low-dimensional space such that the semantic similarities among the nodes can be effectively inferred. Most existing approaches use inner-product of node embedding to measure the similarity between nodes leading to the fact that they lack the capacity to capture complex relationships among nodes. Besides, they take the path in the network just as structural auxiliary information when inferring node embeddings, while paths in the network are formed with rich user informations which are semantically relevant and cannot be ignored. In this paper, We propose a novel method called Network Embedding on the Metric of Relation, abbreviated as NEMR, which can learn the embeddings of nodes in a relational metric space efficiently. First, our NEMR models the relationships among nodes in a metric space with deep learning methods including variational inference that maps the relationship of nodes to a gaussian distribution so as to capture the uncertainties. Secondly, our NEMR considers not only the equivalence of multiple-paths but also the natural order of a single-path when inferring embeddings of nodes, which makes NEMR can capture the multiple relationships among nodes since multiple paths contain rich user information, e.g., age, hobby and profession. Experimental results on several public datasets show that the NEMR outperforms the state-of-the-art methods on relevant inference tasks including link prediction and node classification.

We basically train machines so as to include some kind of automation in it. In machine learning, we use various kinds of algorithms to allow machines to learn the relationships within the data provided and make predictions using them. So, the kind of model prediction where we need the predicted output is a continuous numerical value, it is called a regression problem. Regression analysis convolves around simple algorithms, which are often used in finance, investing, and others, and establishes the relationship between a single dependent variable dependent on several independent ones. For example, predicting house price or salary of an employee, etc are the most common regression problems.

Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or finance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.

During the last two decades, distributed energy systems, especially renewable energy sources (RES), have become more economically viable with increasing market share and penetration levels on power systems. In addition to decarbonization and decentralization of energy systems, digitalization has also become very important. The use of artificial intelligence (AI), advanced optimization algorithms, Industrial Internet of Things (IIoT), and other digitalization frameworks makes modern power system assets more intelligent, while vulnerable to cybersecurity risks. This paper proposes the concept of the Internet of Predictable Things (IoPT) that incorporates advanced data analytics and machine learning methods to increase the resiliency of cyber-physical systems against cybersecurity risks. The proposed concept is demonstrated using a cyber-physical system testbed under a variety of cyber attack scenarios as a proof of concept (PoC).

Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are "black boxes" which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating "local" explanations. We present GLocalX, a "local-first" model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLocalX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLocalX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLocalX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.

This document entails a progressive report on the design and implementation of a bus tracking and monitoring system . This report has its contents within the limits of five chapters with each concisely exploring their various objectives. Chapter one is the introductory chapter. It entails a brief description of a bus tracking and monitoring system ,the need and the aims and objectives of this project. Chapter two consists the literature review of this project. This entails the critical analysis of previous related research and projects undertaken by other people. The merits and demerits of the various implementations.Chapter three consists of theory and design considerations of the proposed system for Kwame Nkrumah University campus. Chapter four talks about the methods used to collect data and the approach and technology stack adopted to build the proposed system.Chapter five concludes the thesis and discusses the results of test and deployment of the proposed system on Kwame Nkrumah University of Science and Technology campus

Approximate Bayesian Computation (ABC) now serves as one of the major strategies to perform model choice and parameter inference on models with intractable likelihoods. An essential component of ABC involves comparing a large amount of simulated data with the observed data through summary statistics. To avoid the curse of dimensionality, summary statistic selection is of prime importance, and becomes even more critical when applying ABC to mechanistic network models. Indeed, while many summary statistics can be used to encode network structures, their computational complexity can be highly variable. For large networks, computation of summary statistics can quickly create a bottleneck, making the use of ABC difficult. To reduce this computational burden and make the analysis of mechanistic network models more practical, we investigated two questions in a model choice framework. First, we studied the utility of cost-based filter selection methods to account for different summary costs during the selection process. Second, we performed selection using networks generated with a smaller number of nodes to reduce the time required for the selection step. Our findings show that computationally inexpensive summary statistics can be efficiently selected with minimal impact on classification accuracy. Furthermore, we found that networks with a smaller number of nodes can only be employed to eliminate a moderate number of summaries. While this latter finding is network specific, the former is general and can be adapted to any ABC application.