Theoretical results on a weightless neural classifier and application to computational linguistics

WiSARD é um classificador n-upla, historicamente usado em tarefas de reconhecimento de padrões em imagens em preto e branco. Infelizmente, não era comum que este fosse usado em outras tarefas, devido á sua incapacidade de arcar com grandes volumes de dados por ser sensível ao conteúdo aprendido. Recentemente, a técnica de bleaching foi concebida como uma melhoria à arquitetura do classificador n-upla, como um meio de coibir a sensibilidade da WiSARD. Desde então, houve um aumento na gama de aplicações construídas com este sistema de aprendizado. Pelo uso frequente de corpora bastante grandes, a etiquetação gramatical multilíngue encaixa-se neste grupo de aplicações. Esta tese aprimora o mWANN-Tagger, um etiquetador gramatical sem peso proposto em 2012. Este texto mostra que a pesquisa em etiquetação multilíngue com WiSARD foi intensificada através do uso de linguística quantitativa e que uma configuração de parâmetros universal foi encontrada para o mWANN-Tagger. Análises e experimentos com as bases da Universal Dependencies (UD) mostram que o mWANN-Tagger tem potencial para superar os etiquetadores do estado da arte dada uma melhor representação de palavra. Esta tese também almeja avaliar as vantagens do bleaching em relação ao modelo tradicional através do arcabouço teórico da teoria VC. As dimensões VC destes foram calculadas, atestando-se que um classificador n-upla, seja WiSARD ou com bleaching, que possua N memórias endereçadas por n-uplas binárias tem uma dimensão VC de exatamente N (2n − 1) + 1. Um paralelo foi então estabelecido entre ambos os modelos, onde deduziu-se que a técnica de bleaching é uma melhoria ao método n-upla que não causa prejuízos à sua capacidade de aprendizado.WiSARD é um classificador n-upla, historicamente usado em tarefas de reconhecimento de padrões em imagens em preto e branco. Infelizmente, não era comum que este fosse usado em outras tarefas, devido á sua incapacidade de arcar com grandes volumes de dados por ser sensível ao conteúdo aprendido. Recentemente, a técnica de bleaching foi concebida como uma melhoria à arquitetura do classificador n-upla, como um meio de coibir a sensibilidade da WiSARD. Desde então, houve um aumento na gama de aplicações construídas com este sistema de aprendizado. Pelo uso frequente de corpora bastante grandes, a etiquetação gramatical multilíngue encaixa-se neste grupo de aplicações. Esta tese aprimora o mWANN-Tagger, um etiquetador gramatical sem peso proposto em 2012. Este texto mostra que a pesquisa em etiquetação multilíngue com WiSARD foi intensificada através do uso de linguística quantitativa e que uma configuração de parâmetros universal foi encontrada para o mWANN-Tagger. Análises e experimentos com as bases da Universal Dependencies (UD) mostram que o mWANN-Tagger tem potencial para superar os etiquetadores do estado da arte dada uma melhor representação de palavra. Esta tese também almeja avaliar as vantagens do bleaching em relação ao modelo tradicional através do arcabouço teórico da teoria VC. As dimensões VC destes foram calculadas, atestando-se que um classificador n-upla, seja WiSARD ou com bleaching, que possua N memórias endereçadas por n-uplas binárias tem uma dimensão VC de exatamente N (2n − 1) + 1. Um paralelo foi então estabelecido entre ambos os modelos, onde deduziu-se que a técnica de bleaching é uma melhoria ao método n-upla que não causa prejuízos à sua capacidade de aprendizado

Weightless neural networks for face recognition

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The interface with the real-world has proved to be extremely challenging throughout the past 70 years in which computer technology has been developing. The problem initially is assumed to be somewhat trivial, as humans are exceptionally skilled at interpreting real-world data, for example pictures and sounds. Traditional analytical methods have so far not provided the complete answer to what will be termed pattern recognition. Biological inspiration has motivated pattern recognition researchers since the early days of the subject, and the idea of a neural network which has self-evolving properties has always been seen to be a potential solution to this endeavour. Unlike the development of computer technology in which successive generations of improved devices have been developed, the neural network approach has been less successful, with major setbacks occurring in its development. However, the fact that natural processing in animals and humans is a voltage-based process, devoid of software, and self-evolving, provides an on-going motivation for pattern recognition in artificial neural networks. This thesis addresses the application of weightless neural networks using a ranking pre-processor to implement general pattern recognition with specific reference to face processing. The evaluation of the system will be carried out on open source databases in order to obtain a direct comparison of the efficacy of the method, in particular considerable use will be made of the MIT-CBCL face database. The methodology is cost effective in both software and hardware forms, offers real-time video processing, and can be implemented on all computer platforms. The results of this research show significant improvements over published results, and provide a viable commercial methodology for general pattern recognition

On Improving Robustness of Hardware Security Primitives and Resistance to Reverse Engineering Attacks

The continued growth of information technology (IT) industry and proliferation of interconnected devices has aggravated the problem of ensuring security and necessitated the need for novel, robust solutions. Physically unclonable functions (PUFs) have emerged as promising secure hardware primitives that can utilize the disorder introduced during manufacturing process to generate unique keys. They can be utilized as \textit{lightweight} roots-of-trust for use in authentication and key generation systems. Unlike insecure non-volatile memory (NVM) based key storage systems, PUFs provide an advantage -- no party, including the manufacturer, should be able to replicate the physical disorder and thus, effectively clone the PUF. However, certain practical problems impeded the widespread deployment of PUFs. This dissertation addresses such problems of (i) reliability and (ii) unclonability. Also, obfuscation techniques have proven necessary to protect intellectual property in the presence of an untrusted supply chain and are needed to aid against counterfeiting. This dissertation explores techniques utilizing layout and logic-aware obfuscation. Collectively, we present secure and cost-effective solutions to address crucial hardware security problems

Recent Advances in Deep Learning Techniques for Face Recognition

In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we summarize 168 recent contributions from this area. We discuss the papers related to different algorithms, architectures, loss functions, activation functions, datasets, challenges, improvement ideas, current and future trends of DL-based FR systems. We provide a detailed discussion of various DL methods to understand the current state-of-the-art, and then we discuss various activation and loss functions for the methods. Additionally, we summarize different datasets used widely for FR tasks and discuss challenges related to illumination, expression, pose variations, and occlusion. Finally, we discuss improvement ideas, current and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp. 99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613

Integrated Waste Management

This book reports research on policy and legal issues, anaerobic digestion of solid waste under processing aspects, industrial waste, application of GIS and LCA in waste management, and a couple of research papers relating to leachate and odour management

Para-images: Cultural ideas and technical apparatuses beyond the pictorial surfaces

Our world is hinged on images. The mass obsession with selfies and spectacles, the surveillance technology and Deepfake videos enabled by computer vision, the Event Horizon Telescope that produced the first image of a black hole, the simulations which climate change research relies on. Reality is being ever more entangled with image, yet images are increasingly detached from the physical world and escape human comprehension. It is obvious that the traditional understanding of images as a representation of the world, while valid, will no longer suffice to account for the intertwined relationship images has with our world. Contemplating the ever-complex relationship between images and reality, the thesis proposes a new approach to understanding images in contemporary visual culture: para-images. The thesis employs Vilém Flusser’s notion of counter vision to examine cultural ideas and technical apparatuses operating beyond the pictorial surfaces of seven images of water splashes. In the process, the thesis identifies agential realism and twenty-first-century media as two useful frameworks in formulating the triangular relationship among humans, images and the world. Attempting to answer the question ‘What is left of an image if the pictorial surface is scratched away?’, the thesis uncovers the often neglected ideological and technical infrastructures that make images possible in the first place. Situating images and machines at the same level of humans as entities with their own agencies, the image theory this thesis establishes concerns the entanglement of humans, machines, apparatuses, images and the world. In short, an image is the world, the world is an image

Development and Evaluation of Biocompatible Engineered Nanoparticles for Use in Ophthalmology

The synthesis and design of biocompatible nanoparticles for targeted drug delivery and bioimaging requires knowledge of both their potential toxicity and their transport. For both practical and ethical reasons, evaluating exposure via cell studies is a logical precursor to in vivo tests. As a step towards clinical trials, this work extensively investigated the toxicity of gold nanoparticles (Au NPs) and carbon dot (CD) nanoparticles as a prelude to their in vivo application, focusing specifically on ocular cells. As a further step, it also evaluated their whole-body transport in mice. The research pursued two approaches in assessing the toxicity of engineered nanoparticles and the suitability of their use in targeted delivery and bioimaging applications: (1) In vitro (using retinal pigment epithelial, corneal, and lens epithelial cells (2) In vivo (mouse whole body studies). Part. 1. In the in vitro assessments of Part 1, the biocompatibilities of spherical, rod, and cubic shaped Au NPs were compared for different exposure concentrations. Spherical Au NPs were evaluated in particular detail, and a possible toxicity mechanism was proposed, based on the findings of a colorimetric assay, electrical impedance measurements, and confocal imaging analysis. The assay measured the activity of succinate hydrogenase, a mitochondrial enzyme, while electrical impedance spectroscopy quantified the strength of cell-cell and cell-substrate attachment, a proxy of viability. Finally, confocal imaging analysis verified that the NPs were internalized and confirmed the degree of their toxicity. Collectively, the data indicated that surface area concentration was the critical toxicity parameter. Subsequently, to create biocompatible Au NPs, a unique end-thiolation of hyaluronic acid was adapted to create homogenously coated Au NPs. The end-thiolated hyaluronate (HS-HA) coating not only improved the biocompatibility of the Au NPs but also enhanced the internalization rate of the larger Au NPs, which could not enter the cells otherwise. The first part of this research also studied the synthesis of biocompatible deep red-emissive CDs for bioimaging applications. For this purpose, a central-composite design response surface methodology (CCD-RSM) was utilized. A scalable isolation-free microwave pyrolysis method for synthesizing deep red-emissive nitrogen-doped carbon dots (nCDs) from citric acid and ethylenediamine was successfully developed and optimized. The formation of C‒N and the presence of pyrrolic N content proved to be keys to creating red-emissive nCDs. Confocal images demonstrated that the nanoparticles could enter healthy corneal, retinal, and lens epithelial ocular cells, as well as cancerous Chinese Hamster Ovary cells. Part 2. Building on the results of in-vitro testing of the engineered Au NPs and nCDs, in Part 2 we developed protocols for injecting both types of NPs in-vivo. Prior to any intravenous or intravitreal injections, a preliminary study tested the ability of Au NPs to cross the tight junctions between retinal pigment epithelial cells. Transwell® permeable supports were used to simulate the blood-retinal barrier (BRB). The results showed that 20 nm Au NPs successfully crossed the permeable supports covered with confluent retinal pigment epithelial cells. Based on this finding, both intravitreal and intravenous injections of nascent and HS-HA coated Au NPs were tested. The intravitreal injections caused retinal detachment, very probably due to the mechanical intrusion of the injection needle and the volume, albeit small, of the injected NPs. Far more significant and encouraging, intravenously injected coated and uncoated NPs successfully crossed the BRB. As a result of the intravenous injections, it was observed that both coated and uncoated Au NPs were able to cross the blood-retinal barrier. As expected, the numbers of HS-HA-coated Au NPs were significantly higher in specific parts of the retina that contain more CD44 expressing cells, which have cell surface receptors for internalizing HA. Finally, based on the confocal imaging analysis, the NP concentration in each retinal layer was quantified as a function of time, post-injection. The NPs reached the retina in less than 5 minutes and reached a maximum concentration within approximately 20 minutes. Due to the enhanced retention and permeability effect of NPs, 8.5% of the uncoated and 12.1% of the HA-coated NPs that reach the retina remained after 24 hours. Next, nCDs with and without the HA coating were injected subcutaneously into post-mortem mouse and porcine eye globes. Ex-vivo porcine eye images showed that intravitreally injected nCDs had effectively diffused through the vitreous to the cornea, and post-mortem whole-body mouse images also demonstrated that the nCDs are suitable for bioimaging, excitable in the NIR region with the sensitivity of 15%. Cumulatively, our observations indicate that HA coated NPs could potentially deliver other payloads such as DNAs, mRNAs, proteins, siRNAs, and drugs into the cells which overexpress CD44 receptors, for example, cancerous and inflammatory cells, thus providing a platform for targeted treatment and imaging of many severe vision-threatening diseases and degenerative conditions

Las Vegas Daily Optic, 04-15-1907

https://digitalrepository.unm.edu/lvdo_news/2788/thumbnail.jp

Balance-guaranteed optimized tree with reject option for live fish recognition

This thesis investigates the computer vision application of live fish recognition, which is needed in application scenarios where manual annotation is too expensive, when there are too many underwater videos. This system can assist ecological surveillance research, e.g. computing fish population statistics in the open sea. Some pre-processing procedures are employed to improve the recognition accuracy, and then 69 types of features are extracted. These features are a combination of colour, shape and texture properties in different parts of the fish such as tail/head/top/bottom, as well as the whole fish. Then, we present a novel Balance-Guaranteed Optimized Tree with Reject option (BGOTR) for live fish recognition. It improves the normal hierarchical method by arranging more accurate classifications at a higher level and keeping the hierarchical tree balanced. BGOTR is automatically constructed based on inter-class similarities. We apply a Gaussian Mixture Model (GMM) and Bayes rule as a reject option after the hierarchical classification to evaluate the posterior probability of being a certain species to filter less confident decisions. This novel classification-rejection method cleans up decisions and rejects unknown classes. After constructing the tree architecture, a novel trajectory voting method is used to eliminate accumulated errors during hierarchical classification and, therefore, achieves better performance. The proposed BGOTR-based hierarchical classification method is applied to recognize the 15 major species of 24150 manually labelled fish images and to detect new species in an unrestricted natural environment recorded by underwater cameras in south Taiwan sea. It achieves significant improvements compared to the state-of-the-art techniques. Furthermore, the sequence of feature selection and constructing a multi-class SVM is investigated. We propose that an Individual Feature Selection (IFS) procedure can be directly exploited to the binary One-versus-One SVMs before assembling the full multiclass SVM. The IFS method selects different subsets of features for each Oneversus- One SVM inside the multiclass classifier so that each vote is optimized to discriminate the two specific classes. The proposed IFS method is tested on four different datasets comparing the performance and time cost. Experimental results demonstrate significant improvements compared to the normal Multiclass Feature Selection (MFS) method on all datasets