Reviewerly/exHarmony huggingface dataset:
Multi-Exposure Image Compression Considering Rate-Distortion Optimization in Rendered High Dynamic Range Image

High-dynamic range (HDR) images, which provide realistic visual perception, have attracted considerable attention in various applications. A simple method for obtaining HDR images is to fuse multiple low-dynamic range (LDR) images captured under varying exposures. We proposed a method of efficiently encoding multi-exposure images using multi-view High Efficiency Video Coding (MV-HEVC) along with intensity mapping function (IMF) to create high-quality HDR images. The inter-view prediction in MV-HEVC eliminates redundancy between multi-exposure images. To achieve high-efficiency MV-HEVC-based multi-exposure image coding, two modifications to the rate-distortion optimization (RDO) process performed during encoding were proposed. First, the distortion in the rendered HDR image is used to replace the original distortion, which depends on the LDR image. Second, to balance distortion and rate in RDO, a new Lagrange multiplier is derived from the derivative of the new distortion with respect to the rate. A model of the distortion in the HDR domain was constructed and expressed as a function of the distortion in the LDR domain. The proposed scheme utilizes the conventional 8-bit codec to compress multi-exposure LDR images and generate an HDR image in the decoder. The experimental results indicate that the proposed technique outperforms HDR image coding schemes in which HEVC Range Extension and JPEG-XT coding standards are used.
On the Development of a Protection Profile Module for Encryption Key Management Components

The ability of a cryptographic system to protect information from attacks depends on many factors, including the secrecy of the encryption key. A crucial aspect of any cryptosystem is how it manages the encryption keys. Encryption Key Management (EKM) spans the entire life cycle of the key, including the key’s generation, usage, distribution, renewal, and destruction. Given the security sensitivity, it is desirable to adopt a widely accepted standard when developing an encryption key management system. Through rigorous development of security requirements and following standardized validation, evaluation, and certification, the consumers’ confidence in the security of the EKM system will be enhanced. The Protection Profile (PP), defined in the Common Criteria for Information Technology Security Evaluation (often referred to as Common Criteria or CC), specifies the security functional and assurance requirements for a specific technology. In this work, we propose a PP Module that is the new evolution of the PP covering trusted security features for EKM, which is based on its compliance with the Network Device collaborative Protection Profile (NDcPP). In particular, by analyzing threats and vulnerabilities of EKM systems, corresponding security objectives and requirements are proposed in the PP, along with the specification of evaluation activities. The quantum-safe aspect of key distribution protocols is further investigated to support EKM products with quantum-resistant algorithms and quantum key distribution features. In addition to presenting the development methodology and implementation process for the PP Module of EKM, we distill lessons learned from developing and validating the PP Module to inspire future research efforts on defining security requirements with the CC.
Application of LSGDM Methods

With the rapid development of social media, traditional E-commerce has gradually transitioned to S-commerce. In other words, there is a social relationship between large groups of users in social commerce. This chapter introduces the application of LSGDM methods in social recommendation.
A Method-Level Defect Prediction Approach Based on Structural Features of Method-Calling Network

Software defect prediction models help testers find program modules that have a high probability of having defects. A method-calling network can express the dependencies between methods in a program. Existing approaches do not sufficiently utilize method-calling network to characterize the structural features between methods. To address this problem, in this study, it is proposed for the first time that the characteristics of methods in a program are obtained by analyzing the method-calling network, and a new approach is proposed for defect prediction at the method-level. Specifically in this study, the method-calling network of the program was first constructed and the network metrics of the method-calling network were obtained. Next, the new network embedding technique (node2vec) was used to automatically encode the method-calling network structure into a low-dimensional vector to obtain the network embedding metrics. Finally, they were combined with code metrics to construct defect prediction models. We evaluated our approaches on 13 open-source software systems. The experimental results show that the proposed method improved the values of area under the receiver operating characteristic curve by 2.5% to 6.7% and Matthews correlation coefficient by 13% to 178.4% compared to the baselines. Therefore the method-calling network contains rich structural features between methods, and the structural features extend the features used for method-level defect prediction and further improve the performance of defect prediction models.
Electronic Safety System for Table Saw

This paper proposes a safety sensor for table saw, based on impedance measurement between two electrodes placed in front of blade.A multitone system is developed, acting as a selective barrier able to distinguish the proximity of a hand with respect to wood, both dry and wet.Due to conductivity of human body, the proximity of the hand introduces a shielding effect, causing a reduction in capacitive coupling between electrodes.On the contrary, wood cannot induce the same effect for all frequencies, even if it is wet.This results in achieving a very sensitive and cost-effective safety system, able to respond effectively to hazard situations.An electronic prototype, realized through digital synchronous detection of two tones at 10 kHz and 1 MHz, demonstrates the effectiveness of the proposed sensing technique.To better describe the working principles, a simple system simulator is studied, able to describe the experimental results.The proposed technique opens the way to a new generation of safety systems for table saw.
Large-Scale Group Decision-Making Considering Trust Behavior

The application of trust in GDM consensus was introduced in the previous chapters. This chapter mainly introduces the role of trust in LSGDM, such as trust based clustering analysis, non cooperative behavior analysis and large-scale group consensus optimization.
The Weighted Markov-Dubins Problem

In this letter, a variation of the classical Markov-Dubins problem is considered, which deals with curvature-constrained least-cost paths in a plane with prescribed initial and final configurations, different bounds for the sinistral and dextral curvatures, and penalties <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu _{L}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu _{R}$</tex-math></inline-formula> for the sinistral and dextral turns, respectively. The addressed problem generalizes the classical Markov-Dubins problem and the asymmetric sinistral/dextral Markov-Dubins problem. The proposed formulation can be used to model an Unmanned Aerial Vehicle (UAV) with a penalty associated with a turn due to the required additional thrust to maintain altitude and airspeed while turning, or a UAV with different curvature bounds and costs for the sinistral and dextral turns due to hardware failures. Using optimal control theory, the main result of this letter shows that the optimal path belongs to a set of at most 21 candidate paths, each comprising of at most five segments. Unlike in the classical Markov-Dubins problem, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$CCC$</tex-math></inline-formula> path, which is a candidate path for the classical Markov-Dubins problem, is not optimal for the weighted Markov-Dubins problem. Moreover, the obtained list of candidate paths for the weighted Markov-Dubins problem reduces to the standard <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$CSC$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$CCC$</tex-math></inline-formula> paths and the corresponding degenerate paths when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu _{L}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu _{R}$</tex-math></inline-formula> approach zero.
Edges and hues keeping denoising algorithm for color images

Based on the unique color separation characteristics of HSI space, noise removal of color images contaminated by high noise is investigated. The color image is first projected into the color feature space HSI, and the color information is separated from the luminance feature information, and then the color components H and S in this space are processed by the distance threshold denoising method in polar coordinates to remove the noise without distorting the color. At the same time, the luminance feature component I is guided filtered to effectively remove the noise while preserving the edge information of the original image; finally, the corresponding inverse conversion is performed to obtain the final color image. The algorithm has some improvement in eliminating color noise, maintaining hue, and protecting image details compared with previous algorithms, which is more in line with the visual requirements of human eyes.
Fine-Scale Face Fitting and Texture Fusion With Inverse Renderer

3D face reconstruction from a single image still suffers from low accuracy and inability to recover textures in invisible regions. In this paper, we propose a method for generating a 3D portrait with complete texture. The coarse face-and-head model and texture parameters are obtained using 3D Morphable Model fitting. We design an image-geometric inverse renderer that acquires normal, albedo, and light to jointly reconstruct the facial details. Then, we use a texture fusion network to extract the valid texture from rendered faces employing different viewpoints. Specifically, this fused texture recovers the invisible region of the input face, which illustrates the realistic surface of our 3D geometric model. Our approach faithfully reconstructs the original face details, including the profiles and the head region. Extensive experiments are performed to demonstrate that our method outperforms state-of-the-art techniques in various challenging scenarios.
A new medical image transfer and process platform

This paper designs a new platform for medical image transfer and process. The hardware part of the platform is based on FPGA, USB and RS232. The software part is based on Windows platform, basing on MFC dialog application developed with C + + programming tools in Microsoft Visual Studio 2010. The platform collects images from medical devices and uses the hardware platform to transmit them to PC for display, the application program on PC can realize the image data process and analysis.
Clustering spatial networks through latent mixture models

Abstract We consider a Bayesian model-based clustering technique that directly accounts for network relations between territorial units and their position in a geographical space. This proposal is motivated by a practical problem: to design administrative structures that are intermediate between the municipality and the province within an Italian region based on the existence of a relatively (to population) high commuting flow. In our social network model, the commuting flows are explained by the distances between the municipalities, i.e., the nodes, in a 3-dimensional space, where the 2 actual geographical coordinates and the third latent variable are modelled through a Gaussian mixture.
Scenarios of STR Development in the Logic of Socio-Political Changes (Based on the Materials of Stakeholders' Assessment)

The system of secondary vocational education is of strategic importance for the economy, since the quality of training determines the potential of its development. The speed of the emergence of new technologies requires flexibility and structural complexity of educational mechanisms, which is reflected in the decisions of the federal project "Professionalism". The new reality dictates the need for a conceptual understanding of the logic of the development of PDF, the potential and risk-taking of the steps taken and the viability of initiatives. The authors' contribution to the solution of this problem consists in identifying possible scenarios for the development of STR based on the expectations and readiness of stakeholders. The purpose of this article is to analyze possible scenarios for the development of SPE and their assessment from the perspective of key stakeholders – teachers and administrators of training institutions for SPE. Methodology, methods and techniques. The work consists of three main blocks: a theoretical analysis of research on the functioning of the SPO; an empirical part, presented by a description of the author's methodology and data from an expert survey; resulting conclusions containing formulations of basic scenarios for the development of the SPO. The scientific novelty of the research lies in the development of a methodology for analyzing the state and prospects of the development of SPO by a key stakeholder – training institutions for SPO. Practical significance. Conceptual constructions and the obtained practical data can be taken into account when developing educational strategies, individual results are useful for optimizing the educational process in universities that train personnel for vocational education. Conclusions. Changes in the value basis of vocational education, as well as an increase in the motivation of teachers and masters (not only material, but also at the level of understanding and acceptance of ideas) determine the effectiveness of decisions and initiatives.
Organization of educational and patriotic work with cadets of the Irkutsk Fire Technical School of the Ministry of Internal Affairs of the USSR in 1970.

The subject of the research is the peculiarities of educational and patriotic work with cadets of the Irkutsk Fire Technical School of the Ministry of Internal Affairs of the USSR in 1970. A brief analysis of the development and transformation of this type of educational activity is given on the example of the work of structural training units of a fire school. The role and list of staff of the command and teaching staff who carried out educational and patriotic work with cadets during the activity of the educational institution are indicated. The research methodology includes a concrete historical approach (M.V. Astakhov, I.D. Kovalchenko, V.F. Kolomiytsev, A.P. Pronstein, etc.), as well as generalization, comparison, synthesis, classification, specification; analysis of archival documents, legislation and scientific literature; retrospective analysis; method of historical analogies. The novelty of this scientific article lies in the fact that during the scientific research, historical sources, archival data describing in detail the history of the formation and development of educational and patriotic work in the Irkutsk Fire Technical School of the Ministry of Internal Affairs of the USSR have been identified and summarized. The conducted research is of interest to a wide range of readers, since the study of educational-patriotic, educational-pedagogical, structural-organizational and service activities in educational institutions of fire protection of the Ministry of Internal Affairs of the USSR is a very important component in the study of the formation and development of departmental education of the Ministry of Internal Affairs of the USSR on the territory of the Baikal region in the 20th century.
A General Bayesian Functional Spatial Partitioning Method for Multiple Region Discovery Applied to Prostate Cancer MRI

Current protocols to estimate the number, size, and location of cancerous lesions in the prostate using multiparametric magnetic resonance imaging (mpMRI) are highly dependent on reader experience and expertise. Automatic voxel-wise cancer classifiers do not directly provide estimates of number, location, and size of cancerous lesions that are clinically important. Existing spatial partitioning methods estimate linear or piecewise-linear boundaries separating regions of local stationarity in spatially registered data and are inadequate for the application of lesion detection. Frequentist segmentation and clustering methods often require pre-specification of the number of clusters and do not quantify uncertainty. Previously, we developed a novel Bayesian functional spatial partitioning method to estimate the boundary surrounding a single cancerous lesion using data derived from mpMRI. We propose a Bayesian functional spatial partitioning method for multiple lesion detection with an unknown number of lesions. Our method utilizes functional estimation to model the smooth boundary curves surrounding each cancerous lesion. In a Reversible Jump Markov Chain Monte Carlo (RJ-MCMC) framework, we develop novel jump steps to jointly estimate and quantify uncertainty in the number of lesions, their boundaries, and the spatial parameters in each lesion. Through simulation we show that our method is robust to the shape of the lesions, number of lesions, and region-specific spatial processes. We illustrate our method through the detection of prostate cancer lesions using MRI.
A Survey on GPU-Based Visual Trackers

The object tracking problem is a key one in computer vision, and it is critical in a variety of applications such as guided missiles, unmanned aerial vehicles, and video surveillance. Despite several types of research on visual tracking, there are still a number of challenges during the tracking process, including computationally intensive tasks that make real-time object tracking impossible. By offloading computation to the graphics processing unit, we may overcome the processing limitations of visual tracking algorithms (GPU). In this work, object tracking algorithms that use GPU parallel computing are summarized. Firstly, the related works are briefly discussed. Secondly, object trackers are classified, summarized, and analyzed from two aspects: Single Object Tracking(SOT) and Multiple Object Tracking (MOT). Finally, we’ll go through parallel computing—what it is and how it’s used, as well as a strategy for designing a parallel algorithm, various types of methods for analyzing parallel algorithm performance for parallel computers, and how to reformulate computational issues in the language of graphics cards.
Multi-objective Bayesian Optimization for Neural Architecture Search

A novel multi-objective algorithm denoted as MO-BayONet is proposed for the Neural Architecture Search (NAS) in this paper. The method based on Bayesian optimization encodes the candidate architectures directly as lists of layers and constructs an extra feature vector for the corresponding surrogate model. The general method allows to accompany the search for the optimal network by additional criteria besides the network performance. The NAS method is applied to combine classification accuracy with network size on two benchmark datasets here. The results indicate that MO-BayONet is able to outperform an available genetic algorithm based approach.
Hierarchical clinical decision support for breast cancer care empowered with Bayesian networks

Purpose Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction. Design/methodology/approach The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level. Findings The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection. Originality/value The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.
Multilayer Perceptrons with Banach-Like Perceptrons Based on Semi-inner Products – About Approximation Completeness

The paper reconsiders multilayer perceptron networks for the case where the Euclidean inner product is replaced by a semi-inner product. This would be of interest, if the dissimilarity measure between data is given by a general norm such that the Euclidean inner product is not longer consistent to that situation. We prove mathematically that the universal approximation completeness is guaranteed also for those networks where the used semi-inner products are related either to uniformly convex or to reflexive Banach-spaces. Most famous examples of uniformly convex Banach spaces are the spaces $$L_{p}$$ and $$l_{p}$$ for $$1<p<\infty $$ . The result is valid for all discriminatory activation functions including the sigmoid and the ReLU activation.
Human-AI Collaboration to Increase the Perception of VR

Virtual reality (VR) is gaining popularity very fast due to newer solutions that increase user perception. Glasses, sensors, and treadmills are the basic functionality for immersing yourself in a virtual environment. In this paper, we propose a human-AI collaboration for analyzing the newly generated images that can be used for creating worlds. The presented method is based on analyzing different scenes (from simulation and real environment) using generative adversarial networks (GAN) and the communication with the user for assessments of the created new environment. User’s information contributes to the analysis of sample quality and possible rebuilding or retraining of the GAN model. The proposal increases the perception of VR by taking the user’s feelings in creating new environments. For this purpose, we combine GAN with fuzzy soft sets inference to gain the possibility of retraining/remodeling the used neural network. It was examined in theoretical simulation and real-environment case study.