Target-oriented Domain Adaptation for Infrared Image Super-Resolution

Recent efforts have explored leveraging visible light images to enrich texture details in infrared (IR) super-resolution. However, this direct adaptation approach often becomes a double-edged sword, as it improves texture at the cost of introducing noise and blurring artifacts. To address these challenges, we propose the Target-oriented Domain Adaptation SRGAN (DASRGAN), an innovative framework specifically engineered for robust IR super-resolution model adaptation. DASRGAN operates on the synergy of two key components: 1) Texture-Oriented Adaptation (TOA) to refine texture details meticulously, and 2) Noise-Oriented Adaptation (NOA), dedicated to minimizing noise transfer. Specifically, TOA uniquely integrates a specialized discriminator, incorporating a prior extraction branch, and employs a Sobel-guided adversarial loss to align texture distributions effectively. Concurrently, NOA utilizes a noise adversarial loss to distinctly separate the generative and Gaussian noise pattern distributions during adversarial training. Our extensive experiments confirm DASRGAN's superiority. Comparative analyses against leading methods across multiple benchmarks and upsampling factors reveal that DASRGAN sets new state-of-the-art performance standards. Code are available at \url{https://github.com/yongsongH/DASRGAN}.Comment: 11 pages, 9 figure

Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarios

Federated Learning (FL) is a promising technology that enables multiple actors to build a joint model without sharing their raw data. The distributed nature makes FL vulnerable to various poisoning attacks, including model poisoning attacks and data poisoning attacks. Today, many byzantine-resilient FL methods have been introduced to mitigate the model poisoning attack, while the effectiveness when defending against data poisoning attacks still remains unclear. In this paper, we focus on the most representative data poisoning attack - "label flipping attack" and monitor its effectiveness when attacking the existing FL methods. The results show that the existing FL methods perform similarly in Independent and identically distributed (IID) settings but fail to maintain the model robustness in Non-IID settings. To mitigate the weaknesses of existing FL methods in Non-IID scenarios, we introduce the Honest Score Client Selection (HSCS) scheme and the corresponding HSCSFL framework. In the HSCSFL, The server collects a clean dataset for evaluation. Under each iteration, the server collects the gradients from clients and then perform HSCS to select aggregation candidates. The server first evaluates the performance of each class of the global model and generates the corresponding risk vector to indicate which class could be potentially attacked. Similarly, the server evaluates the client's model and records the performance of each class as the accuracy vector. The dot product of each client's accuracy vector and global risk vector is generated as the client's host score; only the top p\% host score clients are included in the following aggregation. Finally, server aggregates the gradients and uses the outcome to update the global model. The comprehensive experimental results show our HSCSFL effectively enhances the FL robustness and defends against the "label flipping attack.

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

An investigation of the use of gradients in imaging, including best approximation and the Structural Similarity image quality measure

The L^2-based mean squared error (MSE) and its variations continue to be the most widely employed metrics in image processing. This is most probably due to the fact that (1) the MSE is simple to compute and (2) it possesses a number of convenient mathematical properties, including differentiability and convexity. It is well known, however, that these L^2-based measures perform poorly in terms of measuring the visual quality of images. Their failure is partially due to the fact that the L^2 metric does not capture spatial relationships between pixels. This was a motivation for the introduction of the so-called Structural Similarity (SSIM) image quality measure [1] which, along with is variations, continues to be one of the most effective measure of visual quality. The SSIM index measures the similarity between two images by combining three components of the human visual system--luminance, contrast, and structure. It is our belief that the structure term, which measures the correlation between images, is the most important component of the SSIM. A considerable portion of this thesis focusses on adapting the L^2 distance for image processing applications. Our first approach involves inserting an intensity-dependent weight function into the integral such that it conforms to generalized Weber's model of perception. We solve the associated best approximation problem and discuss examples in both one- and two-dimensions. Motivated by the success of the SSIM, we also solve the Weberized best approximation problem with an added regularization term involving the correlation. Another approach we take towards adapting the MSE for image processing involves introducing gradient information into the metric. Specifically, we study the traditional L^2 best approximation problem with an added regularization term involving the L^2 distance between gradients. We use orthonormal functions to solve the best approximation problem in both the continuous and discrete setting. In both cases, we prove that the Fourier coefficients remain optimal provided certain assumptions on the orthonormal basis hold. Our final best approximation problem to be considered involves maximizing the correlation between gradients. We obtain the relevant stationarity conditions and show that an infinity of solutions exists. A unique solution can be obtained using two assumptions adapted from [2]. We demonstrate that this problem is equivalent to maximizing the entire SSIM function between gradients. During this work, we prove that the discrete derivatives of the DCT and DFT basis functions form an orthogonal set, a result which has not appeared in the literature to the best of our knowledge. Our study of gradients is not limited to best approximation problems. A second major focus of this thesis concerns the development of gradient-based image quality measures. This was based on the idea that the human visual system may also be sensitive to distortions in the magnitudes and/or direction of variations in greyscale or colour intensities--in other words, their gradients. Indeed, as we show in a persuasive simple example, the use of the L^2 distances between image gradients already yields a significant improvement over the MSE. One naturally wonders whether a measure of the correlation between image gradients could yield even better results--in fact, possibly "better" than the SSIM itself! (We will define what we mean by "better" in this thesis.) For this reason, we pursue many possible forms of a "gradient-based SSIM". First, however, we must address the question of how to define the correlation between the gradient vectors of two images. We formulate and compare many novel gradient similarity measures. Among those, we justify our selection of a preferred measure which, although simple-minded, we show to be highly correlated with the "rigorous" canonical correlation method. We then present many attempts at incorporating our gradient similarity measure into the SSIM. We finally arrive at a novel gradient-based SSIM, our so-called "gradSSIM1", which we argue does, in fact, improve the SSIM. The novelty of our approach lies in its use of SSIM-dependent exponents, which allow us to seamlessly blend our measure of gradient correlation and the traditional SSIM. To compare two image quality measures, e.g., the SSIM and our "gradSSIM1", we require use of the LIVE image database [3]. This database contains numerous distorted images, each of which is associated with a single score indicating visual quality. We suggest that these scores be considered as the independent variable, an understanding that does not appear to be have been adopted elsewhere in the literature. This work also necessitated a detailed analysis of the SSIM, including the roles of its individual components and the effect of varying its stability constants. It appears that such analysis has not been performed elsewhere in the literature. References: [1] Z. Wang, A.C. Bovik, H.R. Sheikh, E.P. Simoncelli. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. [2] P. Bendevis and E.R. Vrscay. Structural Similarity-Based Approximation over Orthogonal Bases: Investigating the Use of Individual Components Functions S_k(x,y). In Aurelio Campilho and Mohamed Kamel, editors, Image Analysis and Recognition - 11th International COnference, ICIAR 2014, Vilamoura, Portugal, October 22-24, 2014, Proceedings, Part 1, volume 8814 of Lecture Notes in Computer Science, pages 55-64, 2014. [3] H.R. Sheikh, M.F. Sabir, and A.C. Bovik. A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms. IEEE Transactions on Image Processing, 15(11):3440-2451, November 2006

Mixture-Based Clustering and Hidden Markov Models for Energy Management and Human Activity Recognition: Novel Approaches and Explainable Applications

In recent times, the rapid growth of data in various fields of life has created an immense need for powerful tools to extract useful information from data. This has motivated researchers to explore and devise new ideas and methods in the field of machine learning. Mixture models have gained substantial attention due to their ability to handle high-dimensional data efficiently and effectively. However, when adopting mixture models in such spaces, four crucial issues must be addressed, including the selection of probability density functions, estimation of mixture parameters, automatic determination of the number of components, identification of features that best discriminate the different components, and taking into account the temporal information. The primary objective of this thesis is to propose a unified model that addresses these interrelated problems. Moreover, this thesis proposes a novel approach that incorporates explainability. This thesis presents innovative mixture-based modelling approaches tailored for diverse applications, such as household energy consumption characterization, energy demand management, fault detection and diagnosis and human activity recognition. The primary contributions of this thesis encompass the following aspects: Initially, we propose an unsupervised feature selection approach embedded within a finite bounded asymmetric generalized Gaussian mixture model. This model is adept at handling synthetic and real-life smart meter data, utilizing three distinct feature extraction methods. By employing the expectation-maximization algorithm in conjunction with the minimum message length criterion, we are able to concurrently estimate the model parameters, perform model selection, and execute feature selection. This unified optimization process facilitates the identification of household electricity consumption profiles along with the optimal subset of attributes defining each profile. Furthermore, we investigate the impact of household characteristics on electricity usage patterns to pinpoint households that are ideal candidates for demand reduction initiatives. Subsequently, we introduce a semi-supervised learning approach for the mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. The integration of the uniform distribution within the inner mixture bolsters the model's resilience to outliers. In the unsupervised learning approach, the minimum message length criterion is utilized to ascertain the optimal number of mixture components. The proposed models are validated through a range of applications, including chiller fault detection and diagnosis, occupancy estimation, and energy consumption characterization. Additionally, we incorporate explainability into our models and establish a moderate trade-off between prediction accuracy and interpretability. Finally, we devise four novel models for human activity recognition (HAR): bounded asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(BAGGM-FSHMM), bounded asymmetric generalized Gaussian mixture-based hidden Markov model~(BAGGM-HMM), asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(AGGM-FSHMM), and asymmetric generalized Gaussian mixture-based hidden Markov model~(AGGM-HMM). We develop an innovative method for simultaneous estimation of feature saliencies and model parameters in BAGGM-FSHMM and AGGM-FSHMM while integrating the bounded support asymmetric generalized Gaussian distribution~(BAGGD), the asymmetric generalized Gaussian distribution~(AGGD) in the BAGGM-HMM and AGGM-HMM respectively. The aforementioned proposed models are validated using video-based and sensor-based HAR applications, showcasing their superiority over several mixture-based hidden Markov models~(HMMs) across various performance metrics. We demonstrate that the independent incorporation of feature selection and bounded support distribution in a HAR system yields benefits; Simultaneously, combining both concepts results in the most effective model among the proposed models

Knowledge and Management Models for Sustainable Growth

In the last years sustainability has become a topic of global concern and a key issue in the strategic agenda of both business organizations and public authorities and organisations. Significant changes in business landscape, the emergence of new technology, including social media, the pressure of new social concerns, have called into question established conceptualizations of competitiveness, wealth creation and growth. New and unaddressed set of issues regarding how private and public organisations manage and invest their resources to create sustainable value have brought to light. In particular the increasing focus on environmental and social themes has suggested new dimensions to be taken into account in the value creation dynamics, both at organisations and communities level. For companies the need of integrating corporate social and environmental responsibility issues into strategy and daily business operations, pose profound challenges, which, in turn, involve numerous processes and complex decisions influenced by many stakeholders. Facing these challenges calls for the creation, use and exploitation of new knowledge as well as the development of proper management models, approaches and tools aimed to contribute to the development and realization of environmentally and socially sustainable business strategies and practices

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done