Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Evaluasi Metode Hierarchical Clustering Berbasis Linkage pada MWMOTE : Studi Kasus Data Akademik Universitas XYZ dan Data UCI

Ketidakseimbangan (Imbalanced) data terjadi pada berbagai macam data termasuk data akademik Universitas XYZ dan data UCI. Kasus tersebut menyebabkan adanya misclassified dikarenakan data mayoritas dominan terhadap data minoritas yang berakibat pada menurunnya nilai akurasi. Metode MWMOTE dapat menjadi pilihan dalam menyelesaikan kasus imbalanced melalui pembobotan dan clustering. Penelitian ini bertujuan menangani permasalahan imbalanced dataset akademik di Universitas XYZ angkatan 2014 dan 2015 dan data UCI dengan mengevaluasi hierarchical clustering. Tujuan tersebut dicapai dengan mengevaluasi tiga metoda hierarchical cluster sebagai salah satu sub proses pada MWMOTE untuk menghasilkan data sintetik yang lebih representatif. Hasil yang didapat dari penelitian ini adalah ketiga metoda AHC tersebut tidak memberikan perbedaan yang signifikan dalam perbaikan akurasi MWMOTE pada data akademik dan 7 data UCI yang diuji dengan one-way ANOVA dengan nilai sig/alpha > 0.0

Compactly supported basis functions as support vector kernels: Capturing feature interdependence in the embedding space

Ph.DDOCTOR OF PHILOSOPH

Multivariate discretization of continuous valued attributes.

The area of Knowledge discovery and data mining is growing rapidly. Feature Discretization is a crucial issue in Knowledge Discovery in Databases (KDD), or Data Mining because most data sets used in real world applications have features with continuously values. Discretization is performed as a preprocessing step of the data mining to make data mining techniques useful for these data sets. This thesis addresses discretization issue by proposing a multivariate discretization (MVD) algorithm. It begins withal number of common discretization algorithms like Equal width discretization, Equal frequency discretization, Naïve; Entropy based discretization, Chi square discretization, and orthogonal hyper planes. After that comparing the results achieved by the multivariate discretization (MVD) algorithm with the accuracy results of other algorithms. This thesis is divided into six chapters, covering a few common discretization algorithms and tests these algorithms on a real world datasets which varying in size and complexity, and shows how data visualization techniques will be effective in determining the degree of complexity of the given data set. We have examined the multivariate discretization (MVD) algorithm with the same data sets. After that we have classified discrete data using artificial neural network single layer perceptron and multilayer perceptron with back propagation algorithm. We have trained the Classifier using the training data set, and tested its accuracy using the testing data set. Our experiments lead to better accuracy results with some data sets and low accuracy results with other data sets, and this is subject ot the degree of data complexity then we have compared the accuracy results of multivariate discretization (MVD) algorithm with the results achieved by other discretization algorithms. We have found that multivariate discretization (MVD) algorithm produces good accuracy results in comparing with the other discretization algorithm

Antropofagia and Constructive Universalism: A Diptych

This study proposes a rethinking of the word-image relation through an examination of Joaquin Torres-García’s Constructive Universalism (ca.1934-1949) and the Brazilian Modernist movement of Antropofagia (1928-ca.1934). By placing both in the close relation of a ‘diptych,’ I argue for a new reading of Torres-García’s visual work as well as a different understanding of Antropofagia. In the first part of this work, I argue, through a close reading and viewing of Torres-García’s work, that the constitutive instability between word/image has been overlooked in favour of, on the one hand, an appropriation in terms of a ‘deviation’ from the canon of Geometric Abstraction and on the other hand as a paradigm of Pre-Columbian, Inca abstraction. Both discursive gestures repress the matter of visual aesthesis. Against this strategy of legibility, I propose a counter-reading through the concepts of ‘graphism’ (Leroi-Gourhan), ‘manuscription’ (Sarabia), the ‘sensory field’ (Lyotard) and the hypericon. These concepts allow contingency to find its way back into Torres-García’s oeuvre in opposition to neo-Classicist misappropriations. Throughout my argument, it will become evident that Torres-García’s paintings bespeak an irrepressible mestizaje, an intertwining of the figural with the abstract. It is this tension animating Torres-García’s work that has been neglected by the disciplining of discourse’s ‘logic of illustration.’ In the second part of the study, I take Antropofagia not so much as a historically determinate period in the narrative of Brazilian Modernism, but as a heuristic for the thinking through of the ‘inconstancy’ of the relation between word and image in its New World Baroque vertigo. This vertigo is politically charged, and amounts to a ‘counter-Conquest’ (Lezama Lima) of the clear and distinct distribution of legibility and visibility inherited through coloniality. The metaphoric economy of cannibalism in Oswald de Andrade’s “Manifesto Antropófago” (1928) in conjunction with the visual work of Tarsila do Amaral and the ‘re-discovery’ of Barroco Mineiro by the Brazilian avant-garde deconstructs the narrative of rupture so as to engage in a complex ‘route to roots’ highlighting the artifice of origin. This same artifice marks Torres-García’s oeuvre, and by ‘closing’ the diptych, I show how abstraction folds back into a Baroque superimposition

Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel

Computational and experimental studies on the reaction mechanism of bio-oil components with additives for increased stability and fuel quality

As one of the world’s largest palm oil producers, Malaysia encountered a major disposal problem as vast amount of oil palm biomass wastes are produced. To overcome this problem, these biomass wastes can be liquefied into biofuel with fast pyrolysis technology. However, further upgradation of fast pyrolysis bio-oil via direct solvent addition was required to overcome it’s undesirable attributes. In addition, the high production cost of biofuels often hinders its commercialisation. Thus, the designed solvent-oil blend needs to achieve both fuel functionality and economic targets to be competitive with the conventional diesel fuel. In this thesis, a multi-stage computer-aided molecular design (CAMD) framework was employed for bio-oil solvent design. In the design problem, molecular signature descriptors were applied to accommodate different classes of property prediction models. However, the complexity of the CAMD problem increases as the height of signature increases due to the combinatorial nature of higher order signature. Thus, a consistency rule was developed reduce the size of the CAMD problem. The CAMD problem was then further extended to address the economic aspects via fuzzy multi-objective optimisation approach. Next, a rough-set based machine learning (RSML) model has been proposed to correlate the feedstock characterisation and pyrolysis condition with the pyrolysis bio-oil properties by generating decision rules. The generated decision rules were analysed from a scientific standpoint to identify the underlying patterns, while ensuring the rules were logical. The decision rules generated can be used to select optimal feedstock composition and pyrolysis condition to produce pyrolysis bio-oil of targeted fuel properties. Next, the results obtained from the computational approaches were verified through experimental study. The generated pyrolysis bio-oils were blended with the identified solvents at various mixing ratio. In addition, emulsification of the solvent-oil blend in diesel was also conducted with the help of surfactants. Lastly, potential extensions and prospective work for this study have been discuss in the later part of this thesis. To conclude, this thesis presented the combination of computational and experimental approaches in upgrading the fuel properties of pyrolysis bio-oil. As a result, high quality biofuel can be generated as a cleaner burning replacement for conventional diesel fuel

A Queer Politics of Imperceptibility: A Philosophy of Resistance to Contemporary Sexual Surveillance

This thesis journeys through a series of events to develop a concept of “imperceptibility” as a mode of resistance to contemporary sexual surveillance. The events I examine include biometric recognition of gender and race at airport security checkpoints, the heteropatriarchal colonial surveillance of Indigenous peoples at Standing Rock, various protest actions, and the political potentials of glitch art. Exploring their unexpected points of connection, my goal is to bring into view acts of resistance against sexual surveillance that already operate below and above the threshold of everyday perception. The project advocates for a philosophy of resistance that underscores the political importance of creating new modes of existence. Rather than engaging in the problematic of devising a new model of subjectivity, I argue that what is needed to escape from contemporary systems of capture and control is to turn from the Self as the primary site of concern and affirm instead the potentials of becoming-imperceptible. Imperceptibility signals not invisibility, but the act of relinquishing identity in favour of moving toward becoming everybody/everything. Far from a homogenizing or unitary endeavour, I propose imperceptibility as a radical celebration of difference that surges a revolutionary desire for social transformation through interconnectedness. Activating Gilles Deleuze and Félix Guattari’s pragmatic philosophy and style of writing, which emphasize multiple relations over binary oppositions, I introduce “a queer politics of imperceptibility” as a conceptual framework that takes a both/and approach to consider resistance. That is, I work with and between the tensions of feminist theories of recognition and Deleuze and Guattari’s nonrepresentational philosophy. I develop this framework in each chapter by mapping a constellation of interacting forces and affective intensities between bodies, both human and non-human. A Queer Politics of Imperceptibility makes an important intervention into the fields of feminist surveillance studies, posthumanism, affect theory, postcolonial theory and queer theory by revealing the ways in which imperceptible relations of resistance cascade into the political to generate new potentials to act in the world