Background  AI adoption is increasing, and the highest increase of AI is seen in service functions, followed by product applications. Moreover, the patent filing is rapidly increasing showing high interest in the technology. Product-based firms developing AI technology may need to develop their business model as a result of the introduction of AI. Research aim This thesis aims to explore how product-oriented companies innovate the business model based on AI development. More specifically, how these technological opportunities can, create value, deliver value, and capture value as a result.    Method This thesis follows a qualitative strategy, mainly being explorative. The thesis follows a single case study approach of Axis Communications. Data collection was mainly conducted with semi-structured interviews. Findings The value creation of AI development is described based on functions and the value-added. The value delivery explores key capabilities of the data pipeline, algorithm development and partners. The value capture described the sales model and compares AI-related solutions with competitors. Conclusions The study concludes that AI-based solutions can be an attributing factor for a product-based company to innovate the business model towards a more service and partner-oriented business model. Keywords: Artificial intelligence, Internet of things, Digital servitization, Business model innovation, Business ecosystem

The present research is conducted to investigate long term (more than two years) free and confined drying shrinkage magnitude and behaviour of self-compacting concrete (SCC) and compare with normal concrete (NC). For all SCCs mixes, Portland cement was replaced with 0-60% of fly ash (FA), fine and coarse aggregates were kept constant at 890 kg/m3 and 780 kg/m3, respectively. Two different water binder ratios of 0.44 and 0.33 were examined for both SCCs and NCs. Fresh properties of SCCs such as filling ability, passing ability, viscosity and resistance to segregation and hardened properties such as compressive and flexural strengths, water absorption and density of SCCs and NCs were also determined. Experimental results of free drying shrinkage obtained from this study together with collected comprehensive database from different sources available in the literature were compared to five existing models, namely the ACI 209R-92 model, BSEN-92 model, ACI 209R-92 (Huo) model, B3 model, and GL2000 model. To assess the quality of predictive models, the influence of various parameters (compressive strength, cement content, water content and relative humidity) on the drying shrinkage strain are studied. An artificial neural network models (ANNM) for prediction of drying shrinkage strains of SCC was developed using the same data used in the existing models. Two ANNM sets namely ANNM1 and ANNM2 with different numbers of hidden layer neurones were constructed. Comparison between the results given by the ANNM1 model and the results obtained by the five existing predicted models were presented. The results showed that, using up to 60% of FA as cement replacement can produce SCC with a compressive strength as high as 30 MPa and low drying shrinkage strain. SCCs long-term drying shrinkage from 356 to 1000 days was higher than NCs. Concrete filled elliptical tubes (CFET) with self-compacting concrete containing FA up to 60% are recommended for use in construction in order to prevent confined drying strain. ACI 209R-92 model provided a better prediction of drying shrinkage compared with the other four models. However, a very high predictability with high accuracy was achieved with the ANNM1 model with a mean of 1.004. Moreover, by using ANNM models, it is easy to insert any of factors effecting drying shrinkage to the input parameters to predict drying shrinkage strain of SCC. / Ministry of Higher Education, Libya

We present a theoretical study that investigates the applicability of a graph theoretical approach to realize various quantum experiments. Crucially, we may represent quantum optical experiments involving tabletop optical elements in terms of highly interpretable, coloured, weighted multi-graphs. We introduce the formalism behind this approach; then through the digital discovery framework PyTheus, we uncover over 100 different quantum experiments which realizes complex, novel quantum states. Towards enhancing our interpretation of the AI-based framework's solutions, we also leverage eXplainable-AI (XAI) techniques from computer vision to investigate what a trained neural network learns about quantum experiments. Crucially, we find that we are able to conceptualize the learned strategies which the neural network applies to optimize for a target quantum property, and discover how the network conceives of its solution. We conclude by presenting an experimental proposal which yields realizable solutions that, for the first time, solves high-dimensional variants of a quantum retrodiction puzzle known as the Mean King's Problem. We, therefore, present a case study which investigates the potential for new scientific discoveries through a joint collaboration between human and artificial intelligence.

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Thesis (M.Sc.Eng.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / The spectra of low piano tones have long been known to display unusual, inharmonic characteristics. Surprisingly, precisely this property allowed for fairly successful generation of artificial piano tones by the frequency modulation (FM) synthesis techniques pioneered at Stanford in the 1970s. Though FM synthesis techniques are largely empirical in nature, the close correspondence between the spectra of the FM simulation of piano tones and their actual spectra suggests the hypothesis that there may be a deeper physical connection between frequency modulation and the timbre of string instruments. Recent, highly sophisticated numerical simulations of the physics of pianos have reproduced these characteristic spectra using a geometrically exact form of the equations for vibrating strings. We take these equations as a starting point for an analytical investigation into the connection between modulated waves and instrumental timbre. The content of the investigation is primarily theoretical, establishing approaches and equations which may be used for further exploration. We proceed by deriving the equations of motion from variational principles. The Lagrangian is given careful treatment so that the transverse and longitudinal directions of motion are clearly separated and their coupling is collected in a single interaction term. Approximate equations of motion are then derived. These are shown to have a structure that supports modulated wave behavior. A general form of the solution is offered which has the form a traveling wave plus a correction term. Understanding the correction term demands the solution of a complex set of nonlinear integral equations, though suggestions are made on how determining certain properties of the integral equations would be sufficient for significant insight into the role of modulation effects in the production of distinct musical timbres. Finally, another form of the solution is offered, one more amenable to iterative solution techniques but less transparent in terms of frequency-domain behavior. A discussion regarding the initial conditions and the requirements for the convergence of either of these forms of the solution is given. / 2999-01-01

This thesis investigates the impact of generative AI technologies on deliberative democracy in Latin America, focusing on the cases of Peru and Brazil. Through qualitative interviews with experts from both countries, this research uses several techniques to process the data collected and intends to draw conclusions from it. The study found that unregulated generative AI is a source of concern for Peruvian and Brazilian participants. Another key finding was that these two countries are not keen on collaborating with other countries in the region to establish a regulatory framework on generative AI. Through the analysis of the five principles of deliberative democracy: citizen participation, inclusivity, accessibility, accountability, and quality of deliberation, this project was able to draw conclusions on which of these dimensions could be impacted by generative AI. The results of the study show that in the case of  Peru, generative AI technologies impact citizen participation and accountability the most.  In the case of Brazil generative AI technologies impact citizen participation, inclusivity, and transparency the most. The most important finding, however, is the commonality shared by Peru and Brazil which signifies that in both countries citizen participation will be impacted by generative AI. The question of how citizen participation will be impacted requires a deeper analysis of the responses of the interviewees as they have both positive and negative perspectives on the issue.

We live in a world that is constantly evolving and changing due to technological advances. With tools that utilize generative artificial intelligence, creation has never been more accessible to everyone, regardless of previous experience. Combined with a new variety of user platforms and streaming services, a whole new landscape content exchange is shaping. But how does this affect the industry and, above all, people who make a living from creating moving images? This study focuses on figuring out different creators' general attitude regarding implementing tools based on GenAI and how this would change their creative process. We also explore the consequences that widespread use can lead to. From the creator’s perspective, threats and opportunities are identified because of technological development, as well as how ethical aspects are affected. By utilizing a qualitative approach, the study conducts in-depth interviews with industry professionals to capture their perceptions and experiences with GenAI. The findings reveal a cautious optimism among participants, acknowledging GenAI’s potential to streamline production processes while also recognizing the necessity for careful management regarding regulations. Finally, the research contributes to a better understanding of how GenAI is reshaping video production and suggests pathways for its responsible integration into the industry.

Commodity brain-computer interfaces (BCI) are beginning to accompany everything from toys and games to sophisticated health care devices. These contemporary interfaces allow for varying levels of interaction with a computer. Not surprisingly, the more intimately BCIs are integrated into the nervous system, the better the control a user can exert on a system. At one end of the spectrum, implanted systems can enable an individual with full body paralysis to utilize a robot arm and hold hands with their loved ones [28, 62]. On the other end of the spectrum, the untapped potential of commodity devices supporting electroencephalography (EEG) and electromyography (EMG) technologies require innovative approaches and further research. This thesis proposes a modularized software architecture designed to build flexible systems based on input from commodity BCI devices. An exploratory study using a commodity EEG provides concrete assessment of the potential for the modularity of the system to foster innovation and exploration, allowing for a combination of a variety of algorithms for manipulating data and classifying results. Specifically, this study analyzes a pipelined architecture for researchers, starting with the collection of spatio temporal brain data (STBD) from a commodity EEG device and correlating it with intentional behaviour involving keyboard and mouse input. Though classification proves troublesome in the preliminary dataset considered, the architecture demonstrates a unique and flexible combination of a liquid state machine (LSM) and a deep belief network (DBN). Research in methodologies and techniques such as these are required for innovation in BCIs, as commodity devices, processing power, and algorithms continue to improve. Limitations in terms of types of classifiers, their range of expected inputs, discrete versus continuous data, spatial and temporal considerations and alignment with neural networks are also identified. / Graduate / 0317 / 0984 / jasoncummer@gmail.com

Esta dissertação apresenta o aperfeiçoamento de um sistema de tradução automática português-inglês. Nosso objetivo principal é criar regras de transferência estrutural entre o par de línguas português e inglês e avaliar, através do uso da métrica de avaliação METEOR, o desempenho do sistema. Para isto, utilizamos um corpus teste criado especialmente para esta pesquisa. Tendo como ponto de partida a relevância de uma correta tradução para os tempos verbais de uma sentença, este trabalho priorizou a criação de regras que tratassem a transferência entre os tempos verbais do português brasileiro para o inglês americano. Devido ao fato de os verbos em português estarem distribuídos por três conjugações, criamos um corpus para cada uma dessas conjugações. O objetivo da criação desses corpora é verificar a aplicação das regras de transferência estrutural entre os tempos verbais em todas as três classes de conjugação. Após a criação dos corpora, mapeamos os tempos verbais em português no modo indicativo, subjuntivo e imperativo para os tempos verbais do inglês. Em seguida, iniciamos a construção das regras de transferência estrutural entre os tempos verbais mapeados. Ao final da construção das regras, submetemos os corpora obedecendo as três classes de conjugação à métrica de avaliação automática METEOR. Os resultados da avaliação do sistema após a inserção das regras apresentaram uma regressão quando comparado a avaliação do sistema no estágio inicial da pesquisa. Detectamos, através de análises dos resultados, que a métrica de avaliação automática METEOR não foi sensível às modificações feitas no sistema, embora as regras criadas sigam a gramática tradicional da língua portuguesa e estejam sendo aplicadas a todas as três classes de conjugação. Apresentamos em detalhes o conjunto de regras sintáticas e os corpora utilizados neste estudo, e que acreditamos serem de utilidade geral para quaisquer sistemas de tradução automática entre o português brasileiro e o inglês americano. Outra contribuição deste trabalho está em discutir os valores apresentados pela métrica METEOR e sugerir que novos ajustes sejam feitos a esses parâmetros utilizados pela métrica. / This dissertation presents the development of a Portuguese-to-English Machine Translation system. Our main objective is creating structural transfer rules between this pair of languages, and evaluate the performance of the system using the METEOR evaluation metric. Therefore, we developed a corpus to enable this study. Taking translation relevance as a starting point, we focused on verbal tenses and developed rules that dealt with transfer between verbal tenses from the Brazilian Portuguese to US English. Due to the fact that verbs in Portuguese are distributed in three conjugations, we created one corpus for each of these conjugations. The objective was to verify the application of structural transfer rules between verbal tenses in each conjugation class in isolation. After creating these corpora, we mapped the Portuguese verbal tenses in the indicative, subjunctive and imperative modes to English. Next, we constructed structural transfer rules to these mapped verbal tenses. After constructing these rules, we evaluated our corpora using the METEOR evaluation metric. The results of this evaluation showed lack of improvement after the insertion of these transfer rules, when compared to the initial stage of the system. We detected that the METEOR evaluation metric was not sensible to these modi_cations made to the system, even though they were linguistically sound and were being applied correctly to the sentences. We introduce in details the set of transfer rules and corpora used in this study, and we believe they are general enough to be useful in any rule-based Portuguese-to-English Machine Translation system. Another contribution of this work lies in the discussion of the results presented by the METEOR metric. We suggest adjustments to be made to its parameters, in order to make it more sensible to sentences variation such as those introduced by our rules.

Main reason of this study is to set forth the internal paradox of the basic approach of the artificial intelligence in the medical field to by discussing on the theoretical and application levels and to suggest solutions in theory and practice against that. In order to rule out the internal paradox in the medical decision support systematic, a new medical model is suggested and based on this, concepts such as disease, health, etiology, diagnosis and treatment are questioned. Meanwhile, with the current scientific data, a simple application sample based on how a decision making system which was set up by fuzzy logic and which is based on the perception of human as a complex adaptive system has been explained. Finally, results of the research about accuracy and validity of this application, current improvements based on the current model and the location on the artificial intelligence theory is discussed.