Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Feature Fusion Framework for Automatic Skin Cancer Diagnostics

Malignant melanoma is the most invasive skin cancer and is currently regarded as one of the deadliest disorders; however, it can be cured more successfully if detected and treated early. Recently, CAD (computer-aided diagnosis) systems have emerged as a powerful alternative tool for the automatic detection and categorization of skin lesions, such as malignant melanoma or benign nevus, in given dermoscopy images. In this paper, we propose an integrated CAD framework for rapid and accurate melanoma detection in dermoscopy images. Initially, an input dermoscopy image is pre-processed by using a median filter and bottom-hat filtering for noise reduction, artifact removal, and, thus, enhancing the image quality. After this, each skin lesion is described by an effective skin lesion descriptor with high discrimination and descriptiveness capabilities, which is constructed by calculating the HOG (Histogram of Oriented Gradient) and LBP (Local Binary Patterns) and their extensions. After feature selection, the lesion descriptors are fed into three supervised machine learning classification models, namely SVM (Support Vector Machine), kNN (k-Nearest Neighbors), and GAB (Gentle AdaBoost), to diagnostically classify melanocytic skin lesions into one of two diagnostic categories, melanoma or nevus. Experimental results achieved using 10-fold cross-validation on the publicly available MED-NODEE dermoscopy image dataset demonstrate that the proposed CAD framework performs either competitively or superiorly to several state-of-the-art methods with stronger training settings in relation to various diagnostic metrics, such as accuracy (94%), specificity (92%), and sensitivity (100%).
Mean Square Stability-based Analysis and Synthesis of Continuous-Time Linear Stochastic Networks

In this paper, we study the problem of stability analysis and controller synthesis of continuous-time linear network systems in the presence of stochastic uncertainty. Stochastic uncertainty is assumed to enter multiplicatively in system dynamics through input and output channels of the plant. We used the mean square notion for stochastic stability to address the analysis and controller synthesis problems of linear network systems. These results generalize existing results on stability analysis and controller synthesis from discrete-time linear systems to continuous-time linear systems with multiplicative uncertainty. Necessary, sufficient conditions for mean square stability are expressed in terms of the input-output property of deterministic or nominal system dynamics captured by the mean square system norm and variance of channel uncertainty. The stability results can also be interpreted as small gain theorem for continuous-time stochastic systems. Linear Matrix Inequalities (LMI)-based optimization formulation is provided for the computation of mean square system norm for stability analysis and controller synthesis. For a special case of single input channel uncertainty, we also prove fundamental limitation results that arise in the mean square stabilization of continuous-time linear systems. Simulation results are presented for nine bus power network model to demonstrate the application of developed framework.
Algebraic synthesis of logical controllers with optimization criteria

Formal methods can strongly contribute to improve dependability of logic controllers during design, by providing means to avoid flaws due to designers' omissions or specifications misinterpretations. This communication presents a formal synthesis method whose goal is to obtain the control laws of a logic system from specifications given in natural language. The formal framework underlying the method is the Boolean algebra of n-variable switching functions. The operations and relations of this algebra enable to represent controller specifications formally, to detect inconsistencies within specifications and to generate control laws with optimization criteria. The application of this formal design method to an example permits to illustrate its main advantages.
Bayesian Proper Orthogonal Decomposition for Learnable Reduced-Order Models With Uncertainty Quantification

Designing and/or controlling complex systems in science and engineering relies on appropriate mathematical modeling of systems dynamics. Classical differential equation-based solutions in applied/computational mathematics are often computationally demanding. Recently, the connection between reduced-order models of high-dimensional differential equation systems and surrogate machine learning models has been explored. However, the focus of both existing reduced-order and machine learning models has been how to best approximate the high-fidelity model of choice. Due to high complexity and often limited training data, it is critical for the models to have reliable uncertainty quantification. In this paper, we propose such a novel framework of Bayesian reduced-order models naturally equipped with uncertainty quantification as it learns the distributions of the parameters of the reduced-order models instead of their point estimates. Specifically, we develop learnable Bayesian proper orthogonal decomposition (BayPOD) that learns the distributions of both the POD projection bases and the mapping from the system input parameters to the projected scores/coefficients so that the learned BayPOD can help predict high-dimensional systems dynamics/fields with reliable uncertainty estimates. The developed BayPOD inherits the capability of embedding physics constraints when learning the POD-based surrogate models, a desirable feature when studying complex systems with limited available data. Furthermore, the proposed BayPOD is an end-to-end solution, which unlike other surrogate-based methods, does not require separate POD and machine learning steps. The results from a real-world case study of the pressure field around an airfoil shows the potential of learnable BayPOD as a new family of reduced-order models with reliable uncertainty estimates.
Development and Comparative Analysis of MWCNT-Polymer Composites for Bioelectronics Application

The development of biosensors is essential while determining the inside body assaying and assessing implants in-vivo applications. Researchers are doing remarkable studies in certain areas, but output for receiving, adaptability and circulation of nutrients are also essential. For examining the similar functionality, multi-walled carbon nanotube epoxy-composite with varying filler percentages is examined and compared using electromechanical and morphological assessment. Mechanical investigations like tensile and flexural tests were performed in addition to the hardness of specimens. The DC conductivity test is performed to determine the charge conductivity along with NMR for measuring the signal responses. The surface characterization is performed to determine the morphological properties of specimens. It is observed that the mechanical properties increased and decreased after exceeding the filler composition to addition to 2% of total weight. In comparison, the conductivity and magnetic resonance enhance to a further extent showing proportional to addition of nanofillers, thus making them potential applications for flexible organic bioelectronics products.
How shopping habits change with artificial intelligence: smart speakers' usage intention

PurposeThe research aims to understand how smart speakers are perceived by their actual and potential users, their attitude towards smart speakers and consequently their intention to use them.Design/methodology/approachThe authors apply a structural equation modelling (SEM) approach to test the research hypotheses through data coming from a structured questionnaire.FindingsThe results show that the higher the importance attributed to usefulness and ease of use, the higher the positive attitude that in turn positively affects the intention to use smart speakers. A significant relationship also emerged between task technology fit and attitude towards smart speakers, as well as between perceived enjoyment and attitude towards smart speakers. Perceived privacy risk, innovativeness and social attraction have been found to not significantly impact attitudes towards smart speakers.Originality/valueAlthough several academic studies have focused on various aspects of smart technologies, only a few studies discuss the factors that push consumers to use smart speakers for activities related to commercial transactions. Therefore, looking at the rapid rise of smart speakers for daily tasks and the gradual acceptance of voice interaction with digital tools, the authors proposed a study about Italian users' intention to use smart speakers. Specifically, to fill the gap in the existing literature, the authors applied a SEM approach to identify utilitarian and hedonic benefits that motivate the use of these devices.
Investigation of magnetoelastic properties of Co-based amorphous alloys by ferromagnetic resonance

Magnetoelastic properties of amorphous ribbons of (Fe/sub x/Co/sub 1-x/)/sub 85/B/sub 15/ alloys were studied at room temperature by using ferromagnetic resonance techniques. Ferromagnetic resonance and antiresonance were applied with respect to different penetration depth of microwaves with the aim to distinguish the contribution of the surface layer and the bulk of magnetostriction effect. Uniaxial static elastic strain was used. Magnetostriction constants were calculated from the field shift of resonance or antiresonance curves. Measured linear magnetostriction constants were compared with the results of the biased transversal susceptibility method. Distinct differences between the surface and the bulk were found for some alloys.
miR‑4732‑5p promotes ovarian cancer mobility by targeting MCUR1

MicroRNAs (miRNAs/miRs) play critical roles in tumor progression. However, the role of miR-4732 and its underlying molecular mechanism in ovarian cancer (OC) remain unclear. In the present study, the high expression of miR-4732 was confirmed to be associated with the mortality of patients with OC following surgery, according to The Cancer Genome Atlas Ovarian Cancer database (TCGA-OV). Additionally, the expression of miR-4732 was positively associated with an increased tendency to exhibit an early TNM stage (IIA, IIB and IIC) of OC, indicating its promotional role in the early stages of tumorigenesis. By performing in vitro gain-of-function experiments, the transient transfection of IGROV1 cells with miR-4732-5p mimics enhanced cell viability according to Cell Counting Kit-8 assay, and cell migration and invasion in Transwell assays. However, though the application of loss-of-function experiments, the transient transfection of IGROV1 cells with miR-4732-5p inhibitors hindered cell viability, cell migration and invasion in vitro. Mitochondrial calcium uniporter regulator 1 (MCUR1) was validated as a downstream direct target of miR-4732-5p through bioinformatics analysis, western blotting and luciferase assays. Therefore, the results of the present study provide evidence that miR-4732-5p may promote OC cell mobility through the direct targeting of the tumor suppressor, MCUR1.
Automatic Database Knob Tuning: A Survey

Knob tuning plays an important role in database optimization, which tunes knob settings to optimize the database performance or improve resource utilization. However, there are several common challenges in knob tuning. First, databases have hundreds of configuration knobs, and it is hard to determine the knobs that cause the performance/resource bottlenecks. Second, most knobs are of continuous values and cause large search space, where heuristic knob tuning may not find high-performance knob settings within limited time. Third, it is increasingly tricky to conduct knob tuning with the proliferation of cloud services, where we need to tune a large number of database instances for various scenarios (e.g., different applications, datasets, and hardware). Recently, many learning-based knob tuning methods are proposed to alleviate those problems. The core idea of learning-based knob tuning is that, with the help of machine learning techniques, it is reasonable to collect knob tuning data, leverage these data to train a knob tuning model, and utilize the tuning model to recommend knob settings for new similar scenarios, so as to achieve the optimization objectives. In this paper, we provide a comprehensive survey on database knob tuning. The pipeline of knob tuning includes knob selection, feature selection, tuning methods, and transfer techniques. First, for knob selection, we introduce the main categories of database knobs and summarize existing knob selection algorithms. Second, for feature selection, we introduce commonly-used tuning features and explain existing feature selection techniques (e.g., runtime metric selection and workload encoding). Third, for tuning methods, we compare four classes of tuning methods, i.e., heuristic methods, Bayesian-optimization methods, deep-learning methods, and reinforcement-learning methods. In particular, we summarize the challenges and discuss how existing methods address those challenges. Moreover, we discuss some transfer techniques that utilize historically well-trained tuning models in new scenarios. Fourth, we discuss the implementation of automatic knob tuning methods in typical systems (e.g., commercial relational databases and Big Data analytics systems). Lastly, we provide some research challenges and future research opportunities. We believe this survey can help researchers better understand the knob tuning problems and existing approaches and further encourage them to solve the remaining problems in automatic knob tuning.
Learning Pose Dynamical System for Contact Tasks under Human Interaction

Robots are expected to execute various operation tasks like a human by learning human working skills, especially for complex contact tasks. Increasing demands for human–robot interaction during task execution makes robot motion planning and control a considerable challenge, not only to reproduce demonstration motion and force in the contact space but also to resume working after interacting with a human without re-planning motion. In this article, we propose a novel framework based on a time-invariant dynamical system (DS), taking into account both human skills transfer and human–robot interaction. In the proposed framework, the human demonstration trajectory was modeled by the pose diffeomorphic DS to achieve online motion planning. Furthermore, the motion of the DS was modified by admittance control to satisfy different demands. We evaluated the method with a UR5e robot in the contact task of the composite woven layup. The experimental results show that our approach can effectively reproduce the trajectory and force learned from human demonstration, allow human–robot interaction safely during the task, and control the robot to return to work automatically after human interaction.
Estimation of Expected Operational Losses: Approaches Based on Heavy Right-Tailed Distributions

This paper employs methodologies that were developed for heavy right-tailed distributions to construct the point and interval estimates of the expected operational losses in the US. These are consistent and unbiased estimates of the mean of the heavy right-tailed loss distribution, whereas those based on normal approximations are not reliable. The estimation methodologies used in this paper exploit the nature of the tail index that measures the degree of tail thickness. To eliminate the small-sample bias in the widely used Hill index estimator, this paper employs an estimator, which is the weighted average of Hill estimators. Furthermore, we employ both m out of n bootstrap and empirical likelihood methods for constructing reliable 95 per cent confidence intervals (CI) for the mean. We provide step-by-step details to show that how these methodologies are applied in practice. The results indicate that the estimates of the expected operational losses of financial institutions in the US are much bigger than the simple sample averages. The 95 per cent CI estimates of the expected losses are asymmetric and much wider than those based on normal approximations. These findings have implications for risk management and economic capital requirements.
Using Sentinel-1 and Google Earth Engine cloud computing for detecting historical flood hazards in tropical urban regions: a case of Dar es Salaam

Abstract This study investigates the potential of freely available Sentinel-1 imagery coupled with Google Earth Engine (GEE) for mapping and monitoring flooding in Dar es Salaam. Sentinel-1 images (n = 55) available during the rainy season (March–May) since 2016 were used and processed in GEE. For separating water and land surfaces, we used a histogram-based automatic thresholding method. The binarization accuracy was assessed using confusion matrix based on 1064 randomly generated points in GEE. Overall accuracy of 95% (Kappa = 0.90) were achieved. Dar es Salaam has experienced flood inundation per flood event on average over an area of 50 km2 in March 2019 and 2021. Territories located along the Ocean and inland water shores, built and bare ground were subject to flooding compared to other land cover types. Flooding inundations have been difficult to detect in the city center. With the current temporal and spatial resolution of Sentinel-1, flood detection in city centers remains a challenge yet. However, Sentinel-1 images, coupled with GEE cloud computing simplified flood mapping and monitoring in a large urban region and this approach can be applied in other large cities and their surroundings for countries where data gap and lack of processing tools are critical challenges.
Large-signal Stability Analysis Using Takagi-Sugeno Fuzzy Model Theory for Fractional Frequency Transmission System

The rapid development of renewable energy requires transmission technology innovation. The fractional frequency transmission system (FFTS) is a promising solution for largescale renewable energy transmission. While the stability analysis becomes urgent affairs for engineering application. Especially for the large-signal disturbance, previous methods on small-signal stability would be inapplicable. Thus, the system-level large-signal stability analysis method of FFTS is proposed considering the system parameters, control strategy of AC/AC converter and PLL in this paper. The state-space Takagi-Sugeno fuzzy model is proposed for Lyapunov function construction. Then the corresponding large-signal stability domain of attraction (LSDOA) could be obtained. In order to overcome the qualitative and approximate problem of traditional stability analysis method, the stability strength and affine angle is introduced initially. Ultimately, considering the influence of system parameters with different-frequency interaction feature, the large-signal stability results of FFTS under different gird faults could be revealed completely. The effectiveness of proposed approach is verified by case and simulation.
Towards the Confirmation of QCD on the Lattice

Lattice Quantum Chromodynamics has made tremendous progress over the last decade. New and improved simulation algorithms and lattice actions enable simulations of the theory with unprecedented accuracy. In the first part of this thesis, novel simulation algorithms for dynamical overlap fermions are presented. The generic Hybrid Monte Carlo algorithm is adapted to treat the singularity in the Molecular Dynamics force, to increase the tunneling rate between different topological sectors and to improve the overall volume scaling of the combined algorithm. With this new method, simulations with dynamical overlap fermions can reach smaller lattice spacings, larger volumes, smaller quark masses, and therefore higher precision than had previously been possible. The second part of this thesis is focused on a large scale simulation aiming to compute the light hadron mass spectrum. This simulation is based on a tree-level Symanzik improved gauge and tree-level improved stout-smeared Wilson clover action. The efficiency of the combination of this action and the improved simulation algorithms used allows to completely control all systematic errors. Therefore, this simulation provides a highly accurate ab initio calculation of the masses of the light hadrons, such as the proton, responsible for 95% of the mass of the visible universe, and confirms Lattice QCD in the light hadron sector.
Collapsed two-way contingency tables and the chi-square reduction principle

SUMMARY The value of the chi-square statistic for homogeneity of rows and columns in a contingency table remains unchanged if the table is collapsed (grouped) over homogeneous rows and columns. The difference between the chi-square values pertaining to the original table and to the collapsed table (the 'chi-square reduction') is shown to have a meaningful canonical decomposition which can guide the researcher in detecting homogeneous rows and columns. The methodology proposed generalizes an earlier methodology suggested by Gilula for the same problem.
Multi-Agent Learning and Bargaining Scheme for Cooperative Spectrum Sharing Process

Recently, the lack of spectrum resources has become a key technical bottleneck to develop the Industrial Internet of Things (IIoT). Based on cognitive radio technology, the cognitive IIoT (CIIoT) paradigm can improve spectrum utilization via opportunistically accessing the idle spectrum bands. In this study, a novel cooperative spectrum sharing scheme is presented for the CIIoT system platform. The main challenge of our scheme is to effectively share the limited spectrum resource via cooperative sensing and dynamic accessing techniques. To achieve a mutually desirable solution for different CIIoT devices, we design a learning game model using the ideas of multi-agent reinforcement learning (MARL) and the negotiated aspirations bargaining solution (NABS). In the learning mechanism, individual CIIoT devices adaptively select their cooperative sensing policy according to the MARL model. In the bargaining mechanism, the available spectrum resource is dynamically shared through the NABS, which is obtained based on the devices’ selected sensing policy. By investigating the contribution of MARL to game theory, the proposed scheme can effectively guide intelligent CIIoT devices toward a socially optimal outcome. Numerical simulation results demonstrate that the normalized device payoff, CIIoT system throughput and device fairness of our approach are better than those of existing benchmark protocols. Finally, we present the key challenges and future direction of our research in the CIIoT system operations.
A Cooperative Silicon Content Dynamic Prediction Method With Variable Time Delay Estimation in the Blast Furnace Ironmaking Process

Online assessment of the molten iron quality of a blast furnace ironmaking process strongly depends on reliable measurement of silicon (Si) content. In this article, we propose a novel cooperative strategy to train the data-driven prediction model and estimate variable time delay (VTD) values. First, a dynamic deep network based on stacked denoising autoencoders (D-SDAE) with the ability to describe process nonlinearity and time-varying is designed for Si estimation. Then, time delay between the process variables and Si, which may disrupt the data distribution pattern (i.e., the real input–output relationship), is recovered in the modeling process. It is an overlooked data feature that occurs due to material transportation times, installation location, and the analysis period of sampling equipment. The VTD values are considered to be the parameters of the D-SDAE model and obtained in the training process by the proposed double-scale collaborative search particle swarm optimization algorithm. The effectiveness of the proposed VTD-based D-SDAE model is validated in a numerical simulation and an industrial ironmaking plant, and a marked improvement in the prediction performance is achieved when VTD information is considered.