Multimodal Adversarial Learning

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models are susceptible to adversarial attacks. In the case of generative models, facial sketches drawn by artists mostly used by law enforcement agencies depend on the ability of the artist to clearly replicate all the key facial features that aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. However, the incorporation of hybrid discriminators, which perform attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network have shown to be powerful tools towards better multi modal learning techniques. In general, our overall approach was aimed at improving target detection systems and the visual appeal of synthesized images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesized image. We synthesized sketches using XDOG filter for the CelebA, Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results overall for different model applications are impressive compared to current state of the art

Review, challenges, design, and development

Peres, F., & Castelli, M. (2021). Combinatorial optimization problems and metaheuristics: Review, challenges, design, and development. Applied Sciences (Switzerland), 11(14), 1-39. [6449]. https://doi.org/10.3390/app11146449In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.publishersversionpublishe

Forest structure from terrestrial laser scanning – in support of remote sensing calibration/validation and operational inventory

Forests are an important part of the natural ecosystem, providing resources such as timber and fuel, performing services such as energy exchange and carbon storage, and presenting risks, such as fire damage and invasive species impacts. Improved characterization of forest structural attributes is desirable, as it could improve our understanding and management of these natural resources. However, the traditional, systematic collection of forest information – dubbed “forest inventory” – is time-consuming, expensive, and coarse when compared to novel 3-D measurement technologies. Remote sensing estimates, on the other hand, provide synoptic coverage, but often fail to capture the fine- scale structural variation of the forest environment. Terrestrial laser scanning (TLS) has demonstrated a potential to address these limitations, but its operational use has remained limited due to unsatisfactory performance characteristics vs. budgetary constraints of many end-users. To address this gap, my dissertation advanced affordable mobile laser scanning capabilities for operational forest structure assessment. We developed geometric reconstruction of forest structure from rapid-scan, low-resolution point cloud data, providing for automatic extraction of standard forest inventory metrics. To augment these results over larger areas, we designed a view-invariant feature descriptor to enable marker-free registration of TLS data pairs, without knowledge of the initial sensor pose. Finally, a graph-theory framework was integrated to perform multi-view registration between a network of disconnected scans, which provided improved assessment of forest inventory variables. This work addresses a major limitation related to the inability of TLS to assess forest structure at an operational scale, and may facilitate improved understanding of the phenomenology of airborne sensing systems, by providing fine-scale reference data with which to interpret the active or passive electromagnetic radiation interactions with forest structure. Outputs are being utilized to provide antecedent science data for NASA’s HyspIRI mission and to support the National Ecological Observatory Network’s (NEON) long-term environmental monitoring initiatives

Shape morphing solar shadings: a review

This paper provides an overview of available innovative shape morphing building skins and their design principles. In particular, the proposed review deals with comfort-related issues associated with dynamic solar shading devices, building integration of smart materials, and morphological analyses related to the most recent shape morphing solar skins. In the first part of the paper, an introduction to the typologies of movement in architecture, its concept and application are presented. An explanation of biomimetic principles together with an overview of user's response to dynamic shading devices is also provided. This is followed by the description of the design principles for shape morphing solar shadings with particular focus on energy and comfort aspects, smart materials and biomimetic principles for efficient movements. A review of most recent developments on the topics of comfort, users' response and control of dynamic shading devices, is presented and summarized in a comparison table. The main technical and mechanical properties of the most diffused smart materials (Shape Memory Alloys, Shape Memory Polymers and Shape Memory Hybrids) that can be used for innovative shape morphing solar skins are illustrated in detail and compared. Biomimetic principles for efficient movements complete this part of the work. The principles illustrated in the previous part of this paper are then used to critically analyse the most recent examples of building integrated shape morphing shadings

A Dialectical Methodology For Decision Support Systems Design

As organizations continue to grow in size, reaching global proportions, they have ever increasing impacts on their environments. Some believe that a much broader array of concerns should be brought into organizational decision-making processes, including greater consideration of social, political, ethical and aesthetic factors (Mitroff and Linstone, 1993; Courtney, 2001). Decision environments such as these are decidedly wicked (Rittel and Webber, 1973). Designing decision support systems in such environments where there is a high level of interconnectedness, issues are overlapping and a multiplicity of stakeholders is involved, is a very complex task. In this dissertation a methodology for the development of a DSS for wicked situations is proposed using the design theory building process suggested by Walls et al. (1992). This proposed theory is based on dialectic theory and the multiple perspective approach suggested by Linstone and Mitroff (1993). The design process consists of identifying relevant stakeholders, their respective worldviews, and conflicts in these worldviews. A design (thesis) and counter design (antithesis) are created, and a prototype systems based on these designs are developed. These prototypes are then presented to the different stakeholder groups who engage in a dialogue which leads to the development of a synthesized design. The process is repeated until all conflicts are resolved or resources are exhausted, and a final system is produced. Using action research and system development research methodologies, the proposed design theory was applied to zoning decision process in Orange County, Florida. The results of this study led to the following: 1. It is feasible to implement the MPDP methodology proposed in this dissertation. 2. The MPDP methodology resulted in a synthesized design that accommodates the different views of the stakeholders. 3. The MPDP methodology is suitable for contentious situations and may not be feasible for structured decisions. 4. Most of the subjects did achieve a more understanding of the decision process. These results suggest that the MPDP design theory can be effective in developing decision support systems in contentious situations