Artificial intelligence for throughput bottleneck analysis – State-of-the-art and future directions

Identifying, and eventually eliminating throughput bottlenecks, is a key means to increase throughput and productivity in production systems. In the real world, however, eliminating throughput bottlenecks is a challenge. This is due to the landscape of complex factory dynamics, with several hundred machines operating at any given time. Academic researchers have tried to develop tools to help identify and eliminate throughput bottlenecks. Historically, research efforts have focused on developing analytical and discrete event simulation modelling approaches to identify throughput bottlenecks in production systems. However, with the rise of industrial digitalisation and artificial intelligence (AI), academic researchers explored different ways in which AI might be used to eliminate throughput bottlenecks, based on the vast amounts of digital shop floor data. By conducting a systematic literature review, this paper aims to present state-of-the-art research efforts into the use of AI for throughput bottleneck analysis. To make the work of the academic AI solutions more accessible to practitioners, the research efforts are classified into four categories: (1) identify, (2) diagnose, (3) predict and (4) prescribe. This was inspired by real-world throughput bottleneck management practice. The categories, identify and diagnose focus on analysing historical throughput bottlenecks, whereas predict and prescribe focus on analysing future throughput bottlenecks. This paper also provides future research topics and practical recommendations which may help to further push the boundaries of the theoretical and practical use of AI in throughput bottleneck analysis

Computational and human-based methods for knowledge discovery over knowledge graphs

The modern world has evolved, accompanied by the huge exploitation of data and information. Daily, increasing volumes of data from various sources and formats are stored, resulting in a challenging strategy to manage and integrate them to discover new knowledge. The appropriate use of data in various sectors of society, such as education, healthcare, e-commerce, and industry, provides advantages for decision support in these areas. However, knowledge discovery becomes challenging since data may come from heterogeneous sources with important information hidden. Thus, new approaches that adapt to the new challenges of knowledge discovery in such heterogeneous data environments are required. The semantic web and knowledge graphs (KGs) are becoming increasingly relevant on the road to knowledge discovery. This thesis tackles the problem of knowledge discovery over KGs built from heterogeneous data sources. We provide a neuro-symbolic artificial intelligence system that integrates symbolic and sub-symbolic frameworks to exploit the semantics encoded in a KG and its structure. The symbolic system relies on existing approaches of deductive databases to make explicit, implicit knowledge encoded in a KG. The proposed deductive database $DS$ can derive new statements to ego networks given an abstract target prediction. Thus, $DS$ minimizes data sparsity in KGs. In addition, a sub-symbolic system relies on knowledge graph embedding (KGE) models. KGE models are commonly applied in the KG completion task to represent entities in a KG in a low-dimensional vector space. However, KGE models are known to suffer from data sparsity, and a symbolic system assists in overcoming this fact. The proposed approach discovers knowledge given a target prediction in a KG and extracts unknown implicit information related to the target prediction. As a proof of concept, we have implemented the neuro-symbolic system on top of a KG for lung cancer to predict polypharmacy treatment effectiveness. The symbolic system implements a deductive system to deduce pharmacokinetic drug-drug interactions encoded in a set of rules through the Datalog program. Additionally, the sub-symbolic system predicts treatment effectiveness using a KGE model, which preserves the KG structure. An ablation study on the components of our approach is conducted, considering state-of-the-art KGE methods. The observed results provide evidence for the benefits of the neuro-symbolic integration of our approach, where the neuro-symbolic system for an abstract target prediction exhibits improved results. The enhancement of the results occurs because the symbolic system increases the prediction capacity of the sub-symbolic system. Moreover, the proposed neuro-symbolic artificial intelligence system in Industry 4.0 (I4.0) is evaluated, demonstrating its effectiveness in determining relatedness among standards and analyzing their properties to detect unknown relations in the I4.0KG. The results achieved allow us to conclude that the proposed neuro-symbolic approach for an abstract target prediction improves the prediction capability of KGE models by minimizing data sparsity in KGs

Who wrote this scientific text?

The IEEE bibliographic database contains a number of proven duplications with indication of the original paper(s) copied. This corpus is used to test a method for the detection of hidden intertextuality (commonly named "plagiarism"). The intertextual distance, combined with the sliding window and with various classification techniques, identifies these duplications with a very low risk of error. These experiments also show that several factors blur the identity of the scientific author, including variable group authorship and the high levels of intertextuality accepted, and sometimes desired, in scientific papers on the same topic

Stochastic Sampling and Machine Learning Techniques for Social Media State Production

The rise in the importance of social media platforms as communication tools has been both a blessing and a curse. For scientists, they offer an unparalleled opportunity to study human social networks. However, these platforms have also been used to propagate misinformation and hate speech with alarming velocity and frequency. The overarching aim of our research is to leverage the data from social media platforms to create and evaluate a high-fidelity, at-scale computational simulation of online social behavior which can provide a deep quantitative understanding of adversaries\u27 use of the global information environment. Our hope is that this type of simulation can be used to predict and understand the spread of misinformation, false narratives, fraudulent financial pump and dump schemes, and cybersecurity threats. To do this, our research team has created an agent-based model that can handle a variety of prediction tasks. This dissertation introduces a set of sampling and deep learning techniques that we developed to predict specific aspects of the evolution of online social networks that have proven to be challenging to accurately predict with the agent-based model. First, we compare different strategies for predicting network evolution with sampled historical data based on community features. We demonstrate that our community-based model outperforms the global one at predicting population, user, and content activity, along with network topology over different datasets. Second, we introduce a deep learning model for burst prediction. Bursts may serve as a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross-platform social media data is valuable for predicting bursts within a single social media platform. An LSTM model is proposed in order to capture the temporal dependencies and associations based upon activity information. These volume predictions can also serve as a valuable input for our agent-based model. Finally, we conduct an exploration of Graph Convolutional Networks to investigate the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of targeted graph convolutional networks. Graph Convolutional Networks are important in the social network context as the sociological and anthropological concept of \u27homophily\u27 allows for the method to use network associations in assisting the attribute predictions in a social network

Diversity Ecosystem: Toward an Understanding of Its Role on Internal Marketing in Diversity Goals and Outcomes

Organizational leaders may announce diversity initiatives, yet often these goals are never fully realized. When organizational leaders establish diversity goals, they frequently rely on internal marketing teams to implement them. Internal Marketing’s ability to influence Diversity Outcomes and Self-Rated Employee Performance may differ due to the moderating effects of the organization’s Diversity Ecosystem. The Diversity Ecosystem is a novel construct that involves an employee’s interpretation of an organization’s openness, aspects of organizational justice, need for diversity and voice. Does the strength of an organization’s Diversity Ecosystem moderate the relationship between Organizational Commitment to Diversity and Internal Marketing? An employee’s level of Job Involvement may also mediate Internal Marketing’s effect on Diversity Outcomes and Self-Rated Employee Performance. In turn, how does an employee’s Job Involvement mediate the relationship between Internal Marketing and Diversity Outcomes and Self-Rated Employee Performance. This research began in the summer of 2020 as organizations faced pressure to diversify after the death of George Floyd. The researcher began the project with a series of qualitative interviews with governmental and corporate leaders to aid in hypothesis development. After initial x qualitative interviews, an initial quantitative survey was developed and sent to as a pretest to full-time employees. From those results and after a thorough literature review, an initial qualitative survey was sent to a pilot sample of full-time employees nationwide. After an analysis of the pilot sample, a quantitative survey was sent to 364 full-time employees across the United States. The survey was designed in Qualtrics and administered through Prolific. The survey data was analyzed through Partial Least Squares Structural Equation Modeling. The development and understanding of a Diversity Ecosystem offer a key academic research contribution and potential managerial implications for diversity efforts

Transparency: from tractability to model explanations

As artificial intelligence (AI) and machine learning (ML) models get increasingly incorporated into critical applications, ranging from medical diagnosis to loan approval, they show a tremendous potential to impact society in a beneficial way, however, this is predicated on establishing a transparent relationship between humans and automation. In particular, transparency requirements span across multiple dimensions, incorporating both technical and societal aspects, in order to promote the responsible use of AI/ML. In this thesis we present contributions along both of these axes, starting with the technical side and model transparency, where we study ways to enhance tractable probabilistic models (TPMs) with properties that enable acquiring an in-depth understanding of their decision-making process. Following this, we expand the scope of our work, studying how providing explanations about a model’s predictions influences the extent to which humans understand and collaborate with it, and finally we design an introductory course into the emerging field of explanations in AI to foster the competent use of the developed tools and methodologies. In more detail, the complex design of TPMs makes it very challenging to extract information that conveys meaningful insights, despite the fact that they are closely related to Bayesian networks (BNs), which readily provide such information. This has led to TPMs being viewed as black-boxes, in the sense that their internal representations are elusive, in contrast to BNs. The first part of this thesis challenges this view, focusing on the question of whether it is feasible to extend certain transparent features of BNs to TPMs. We start with considering the problem of transforming TPMs into alternative graphical models in a way that makes their internal representations easy to inspect. Furthermore, we study the utility of existing algorithms in causal applications, where we identify some significant limitations. To remedy this situation, we propose a set of algorithms that result in transformations that accurately uncover the internal representations of TPMs. Following this result, we look into the problem of incorporating probabilistic constraints into TPMs. Although it is well known that BNs satisfy this property, the complex structure of TPMs impedes applying the same arguments, thus advances on this problem have been very limited. Having said that, in this thesis we provide formal proofs that TPMs can be made to satisfy both probabilistic and causal constraints through parameter manipulation, showing that incorporating a constraint corresponds to solving a system of multilinear equations. We conclude the technical contributions studying the problem of generating counterfactual instances for classifiers based on TPMs, motivated by the fact that BNs are the building blocks of most standard approaches to perform this task. In this thesis we propose a novel algorithm that we prove is guaranteed to generate valid counterfactuals. The resulting algorithm takes advantage of the multilinear structure of TPMs, generalizing existing approaches, while also allowing for incorporating a priori constraints that should be respected by the final counterfactuals. In the second part of this thesis we go beyond model transparency, looking into the role of explanations in achieving an effective collaboration between human users and AI. To study this we design a behavioural experiment where we show that explanations provide unique insights, which cannot be obtained by looking at more traditional uncertainty measures. The findings of this experiment provide evidence supporting the view that explanations and uncertainty estimates have complementary functions, advocating in favour of incorporating elements of both in order to promote a synergistic relationship between humans and AI. Finally, building on our findings, in this thesis we design a course on explanations in AI, where we focus on both the technical details of state-of-the-art algorithms as well as the overarching goals, limitations, and methodological approaches in the field. This contribution aims at ensuring that users can make competent use of explanations, a need that has also been highlighted by recent large scale social initiatives. The resulting course was offered by the University of Edinburgh, at an MSc level, where student evaluations, as well as their performance, showcased the course’s effectiveness in achieving its primary goals

Simulation Intelligence: Towards a New Generation of Scientific Methods

The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence operating system stack (SI-stack) and the motifs therein: (1) Multi-physics and multi-scale modeling; (2) Surrogate modeling and emulation; (3) Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based modeling; (6) Probabilistic programming; (7) Differentiable programming; (8) Open-ended optimization; (9) Machine programming. We believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science