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This paper presents a critical review of a representative pool of 169 scientific papers and 175 patents, from the last twenty years, proposing new eco-assessment software, in order to draw an overview about their main features and functionalities. Each document was analysed in relation to its source (academia or industry), the application field of the assessed product, the type of use, the application phase, the data entry modality, the assessed product life cycle phases and items, the exploited methods and tools for the eco-assessment, the implemented eco-improvement strategies. Then, based on these collected data, all the documents were classified on multiple levels in accordance with the rigorous ontology of the Life Cycle Assessment methodology. While the detailed analysis of the results allowed us to identify their distributions at to date and their time trends. The obtained results highlighted the trend towards the increase in the specificity of the software with respect to the application field, the focus on the quantification of the environmental impacts deriving from the use of the product and the proposed eco-improvement strategies mainly pointing in this direction. The provided outcomes may be used by researchers and professionals for classifying the many available software and identifying the most suitable ones in relation to their specific exigences. • Literature survey of 169 papers and 175 patents about eco-assessment software. • Double investigation perspective about academic and industrial developments. • Tendency to increase the specificity of software according to the application field. • Main and increasing focus on measuring the environmental impacts of product use. • Most eco-improvement strategies are aimed at reducing product's energy consumption. [ABSTRACT FROM AUTHOR]

Highlights: [•] Granular computing aspects of natural computing. [•] Review of different granular soft computing research. [•] Biological motivation, design principles, application areas, open research problems and challenging issues of these models. [Copyright &y& Elsevier]

Abstract: Planning is motivated by acting. Most of the existing work on automated planning underestimates the reasoning and deliberation needed for acting; it is instead biased towards path-finding methods in a compactly specified state-transition system. Researchers in this AI field have developed many planners, but very few actors. We believe this is one of the main causes of the relatively low deployment of automated planning applications. In this paper, we advocate a change in focus to actors as the primary topic of investigation. Actors are not mere plan executors: they may use planning and other deliberation tools, before and during acting. This change in focus entails two interconnected principles: a hierarchical structure to integrate the actorʼs deliberation functions, and continual online planning and reasoning throughout the acting process. In the paper, we discuss open problems and research directions toward that objective in knowledge representations, model acquisition and verification, synthesis and refinement, monitoring, goal reasoning, and integration. [Copyright &y& Elsevier]

Highlights: [•] We deposited IZO by DC Sputtering on heat sensitive substrates such as paper. [•] The materials displayed high electric conductivity and optical transparency. [•] Electrochromic materials were deposited using screen-printing. [•] Solid-state electrochromic devices were assembled, showing an excellent performance. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes the concept and the technology of social networking federation as a paradigm where information on various social network systems can be seamlessly integrated in order to provide users a uniform and semantic view of their social connections. Such a uniformly fused social network provides a single point of access where all information with respect to one’s social networks can be queried and reasoned about. The goal of the research is to establish a foundation of integrating and assimilating information within multiple social network systems. We designed a reference model of social networking federation system in this paper, as well as some prototype application to demonstrate its paradigm. We believe our work would provide a novel vision of future online social network. [Copyright &y& Elsevier]

Plotting data into graphs is a crucial step in data analysis as part of an initial descriptive statistics exercise since it gives the researcher an overview of the shape and nature of the data. Outlier values may also be identified, and these may be incorrect data, or true and important outliers. This paper explains how to access Microsoft Excel's Analysis Toolpak and provides some pointers for the utilisation of the histogram tool within the Toolpak. [ABSTRACT FROM AUTHOR]

The plotting of data into graphs should be a mandatory step in all data analysis as part of a descriptive statistics exercise, since it gives the researcher an overview of the shape and nature of the data. Moreover, outlier values may be identified, which may be incorrect data, or true outliers, from which important findings (and publications) may arise. This exercise should always precede inferential statistics, when possible, and this paper in the Early Human Development WASP series provides some pointers for doing so in Microsoft Excel™. [ABSTRACT FROM AUTHOR]

This work introduces a novel architecture that allows for easy integration of multiple satellites in a single simulation. The new design focuses on modularity, expandability, and easy scriptability while maintaining Basilisk's high-fidelity and speed features. Modularity is important to make highly specialized simulations, which include parameters related to the environment but also specific to each spacecraft. Expandability and scriptability are also key, as one of the goals is to facilitate the creation of simulations with a large number of satellites. The architecture is implemented in Basilisk, an open-source, flight-proven physics and flight software engine, although the fundamental principles can be applied to any software application. Through an overhauled messaging system, the architecture also allows for easy addition of homogeneous or heterogeneous satellites with reduced overhead for the user. This paper shows code snippets and multithreading simulation performance to discuss how the architecture achieves its underlying objectives of simplicity, accessibility, and performance. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to extend the versatile mixed methods originally developed by Chen and Williams for isothermal flows in "Versatile Mixed Methods for the Incompressible Navier-Stokes Equations," Computers & Mathematics with Applications, Volume 80, Number 6, 2020, and use them to simulate non-isothermal incompressible flows. These new mixed methods are particularly interesting, as with only minor modifications they can be applied to a much broader range of flows, including non-isothermal weakly-compressible flows, and fully-compressible flows. In the main body of this paper, we carefully develop these mixed methods for solving the Boussinesq model equations. Thereafter, we establish the L2-stability of the discrete temperature and velocity fields. In addition, we obtain rigorous error estimates for the temperature and velocity fields. Finally, we assess the practical behavior of the methods by applying them to a set of well-known convection problems. [ABSTRACT FROM AUTHOR]

• How does driver behavior expressed through head pose dynamics relate to involvement in safety critical events. • Computer vision and mixed logit techniques used to identify patterns and relationships between drivers' head pose dynamics and crash involvement. • Angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. This paper investigates the role of driver behavior especially head pose dynamics in safety–critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers' head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes. Marginal effects show that angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. Furthermore, traffic flow and lane changing also contribute to increase in likelihood of crash intensity. These findings provide new insights into pre-crash factors, especially human factors and safety–critical events. The study highlights the importance of considering human factors in designing driver assistance systems and developing safer vehicles. This research contributes by examining naturalistic driving data at the microscopic level with early detection of behaviors that lead to SCEs and provides a basis for future research on automation. [ABSTRACT FROM AUTHOR]

Underwater images serve as crucial mediums for conveying marine information. Nevertheless, due to the inherent complexity of the underwater environment, underwater images often suffer from various quality degradation phenomena such as color deviation, low contrast, and non-uniform illumination. These degraded underwater images fail to meet the requirements of underwater computer vision applications. Consequently, effective quality optimization of underwater images is of paramount research and analytical value. Based on whether they rely on underwater physical imaging models, underwater image quality optimization techniques can be categorized into underwater image enhancement and underwater image restoration methods. This paper provides a comprehensive review of underwater image enhancement and restoration algorithms, accompanied by a brief introduction to underwater imaging model. Then, we systematically analyze publicly available underwater image datasets and commonly-used quality assessment methodologies. Furthermore, extensive experimental comparisons are carried out to assess the performance of underwater image optimization algorithms and their practical impact on high-level vision tasks. Finally, the challenges and future development trends in this field are discussed. We hope that the efforts made in this paper will provide valuable references for future research and contribute to the innovative advancement of underwater image optimization. • A comprehensive review of underwater image enhancement and restoration methods is provided. • The publicly available underwater image datasets and underwater image quality metrics are introduced. • The extensive experiments on underwater image enhancement and restoration methods are conducted. • The challenges in the field are discussed and the promising insights for future directions are provided. [ABSTRACT FROM AUTHOR]

• This work proposes a simple loss function for imbalanced image classification. • This work studies the effect of suppressing output scores on incorrect classes for imbalanced image classification. • This work demonstrates the effectiveness of the proposed method through experiments on imbalanced datasets. [Display omitted] Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets. However, imbalanced class distributions still limit the wide applicability of these models due to degradation in performance. To solve this problem, in this paper, we concentrate on the study of cross entropy which mostly ignores output scores on incorrect classes. This work discovers that neutralizing predicted probabilities on incorrect classes improves the prediction accuracy for imbalanced image classification. This paper proposes a simple but effective loss named complement cross entropy based on this finding. The proposed loss makes the ground truth class overwhelm the other classes in terms of softmax probability, by neutralizing probabilities of incorrect classes, without additional training procedures. Along with it, this loss facilitates the models to learn key information especially from samples on minority classes. It ensures more accurate and robust classification results on imbalanced distributions. Extensive experiments on imbalanced datasets demonstrate the effectiveness of the proposed method compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Accurate hand joints detection from images is a fundamental topic that is essential for many applications in computer vision and human–computer interaction. This paper presents a two-stage network for hand joints detection from a single unmarked image by using serial-parallel multi-scale feature fusion. In stage I, the hand regions are located by an encoder-decoder network, and the features of each detected hand region are extracted by a shallow spatial hand features representation module. The extracted hand features are then fed into stage II, which consists of serially connected feature extraction modules with similar structures, called "multi-scale feature fusion" (MSFF). An MSFF contains parallel multi-scale feature extraction branches, which generate initial hand joint heatmaps. The initial heatmaps are then mutually reinforced by the anatomic relationship between hand joints. The hand joint detection accuracy shows that the proposed network overperforms the state-of-the-art methods on current datasets, 1) RHD, 2) HS, 3) MPII & NZSL, 4) DCD8-6000, with the PCK@0.2 of 0.94, 0.92, 0.84, 0.97. Meanwhile, one hand in the image takes between 24 and 37 ms to process, which is adequate for supporting many real-time applications. [ABSTRACT FROM AUTHOR]

Logistics activities were included in the wave of changes brought with the advent of the fourth industrial revolution. Several applications can be recognized for several activities, aiming all at efficiently optimizing processes and consequently production and volumes. Among them, collaborative robots involved for instance in picking, palletizing or assembly operations are quickly spreading, as also demonstrated by the increase in the number of publications available in literature. In response, this paper presents the results of a bibliometric analysis carried out on 64 scientific papers which deal with this topic within the logistics field. Analysis were made through two different software applications, namely Microsoft Excel™ and VOSviewer. Results are quite optimistic as they delineate great opportunities for collaborative robots to establish their position in the industrial context. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) has captured the attention of various smart farming industries in recent years by connecting physical objects of smart farms over the internet so that they can be discovered and queried globally using Enterprise Application Software (EAS). This continuous transmission and processing poses a significant challenge in dealing with the massive amount of Big-Data being gathered and disseminated. As more applications use cloud platforms to increase storage capacity by storing Big-Data on an untrusted cloud server, data security precautions should be taken. However, with the rapid increase in the number of IIoT devices and EAS end-users and their diverse categories, the existing device authentication mechanisms in IIoT suffer from single factor authentication and poor adaptability. In light of the security requirements in IIoT Big-Data sharing using untrusted cloud servers, we present in this paper a sustainable Ethereum merge-based Big-Data gathering and dissemination in IIoT system. Extensive theoretical and simulation results validate data gathering with a business incentive mechanism with a proper data propagation threshold is an important parameter for determining equilibrium and analysing dynamic properties of IIoT system, which directly affects data processing speed and its final state. The Ethereum merge mechanism controls data dissemination instances in both the industry and EAS through classification and integrity verification using distributed authentication devices. Proof-of-stake is used for this purpose, which uses randomly selected validators to confirm transactions and add new ledgers to consortium blockchains to transfer data using partial secrets, making it fast, secure, and sustainable. Our proposed scheme outperforms other state-of-the-art schemes in simulation over Hyperledger Fabric with improved security measures. [ABSTRACT FROM AUTHOR]

We are entering an era where everything is almost connected. According to Gartner, "we expect to see 20 billion internet-connected things by 2020". This evolution has engendered a colossal amount of data that traditional computer applications could no longer handle. We penetrate the epoch of smart applications: intelligent, contextual and proactive. On the other side, a trend that makes the hype is Gamification. It is now being applied to several domains especially those that targets human engagement, performance and sustainability. In this paper, through a systematic mapping study, we will present a breadth-first review of search papers that integrate gamification in Internet of Things (IoT) applications. The main aim is to provide a historical and geographic classification of research that combines IoT with gamification, the most challenging areas, and the artifacts that come out. [ABSTRACT FROM AUTHOR]

Within a large range of applications in computer vision, Human Action Recognition has become one of the most attractive research fields. Ambiguities in recognizing actions does not only come from the difficulty to define the motion of body parts, but also from many other challenges related to real world problems such as camera motion, dynamic background, and bad weather conditions. There has been little research work in the real world conditions of human action recognition systems, which encourages us to seriously search in this application domain. Although a plethora of robust approaches have been introduced in the literature, they are still insufficient to fully cover the challenges. To quantitatively and qualitatively compare the performance of these methods, public datasets that present various actions under several conditions and constraints are recorded. In this paper, we investigate an overview of the existing methods according to the kind of issue they address. Moreover, we present a comparison of the existing datasets introduced for the human action recognition field. [ABSTRACT FROM AUTHOR]

Pavement crack detection continues to be a stubborn problem given the interference of various factors in the actual pavement and the complex topological structure of asphalt pavement. Among all the obstacles, the bottleneck of pavement crack detection lies in the difficulty of segmenting the cracks in the pavement images whose edges are blurred. This paper proposes a multi-scale context feature and cross-attention based on convolutional neural network for accurate and robust pavement crack segmentation. The multi-scale context feature module is built in different deep networks to extract rich crack feature information. Subsequently, in order to effectively promote the seamless integration of features at different levels, we deploy cross-attention modules to each branch. After that, we add deep supervision to each branch to accelerate training. Finally, we integrate the outputs of each branch to obtain the final output diagram. The comparative experiments on various pavement datasets show that the method has better robustness. At the same time, this paper designs a complete system of pavement crack detection (PCD) and develops corresponding engineering application software. The PCD system can record the real-time pavement image data to the edge server, and the client can also monitor the real-time pavement images from the edge server through the HTTP protocol. • A multi-scale context feature module is built in the deep layer for robustly describing the size of the cracks. • The cross-attention module enables each pixel to select individual contextual information from multi-layer features. • In-depth supervision is added to each branch to accelerate the optimization of the network model. • An intelligent detection application software is designed and integrated with multiple modules. [ABSTRACT FROM AUTHOR]

Hydraulic excavators, widely employed in harsh environments, have garnered increased attention in recent years from manufacturers and researchers for automatic operation, particularly through vision-based manipulator pose measurement. This paper utilized a mixed review method to explore the vision-based measurements as well as other applications. A quantitative analysis of computer vision (CV) applications on excavators was performed through a literature search, leading to the classification of these applications into three categories based on keywords. Subsequently, a comprehensive literature review identified four key requirements and challenges for vision-based measurement in the context of automatic control, followed by a discussion on the future prospects of marker-based and deep learning-based manipulator pose measurement for automatic control. This paper offers an in-depth examination of CV applications in excavators, emphasizing the pertinent challenges and forthcoming trends in vision-based manipulator pose measurement for automatic control. • Investigate the applications of computer vision on excavators • Review the literature in 3 aspects: excavator detection and tracking; action recognition; manipulator pose estimation. • Propose requirements and challenges of vision-based measurement approaches from the perspective of automatic control. [ABSTRACT FROM AUTHOR]

Computer vision (CV) technology, a key subset of artificial intelligence, provides powerful tools for extracting valuable insights from visual data, which is a crucial component for the urban planning process. Despite the promising potential of CV in urban planning, its applications in this context have not been thoroughly examined. This lack of scholarship represents a critical knowledge gap in our understanding of the role of CV in urban planning. This paper aims to provide a consolidated understanding in CV applications in the urban planning process and challenges urban planners face during the adoption of CV. The paper conducts a systematic literature review to tackle the questions of how is CV applied in the urban planning process, and what are the challenges in adopting CV techniques and tools for the urban planning process? The findings revealed: (a) CV could support a broad range of urban planning tasks including data collection and analysis, issue identification and prioritisation, public participation, plan design and adoption, and implementation and evaluation; (b) CV could improve decision-making through various visual information, but its limitations need to be considered, and; (c) Utilisation of CV for urban planning could support the efforts in sustainable urban development. This study informs urban policy- and plan-making circles by providing insights into the existing and prospective contributions of CV in the planning process, how CV transforms and augments planning practices, and elaborates the challenges and limitations of CV adoption. [ABSTRACT FROM AUTHOR]

The strip running deviation in steel production can cause significant economic losses by forcing a shutdown of the whole steel production line. However, due to the fast running speed (100–140 m/min) of the strip, it a difficult problem to accurately judge online whether the strip running deviation or not and control its deviation during operation. In this paper, a fast and accurate model for detecting strip running deviation is proposed, this model allows for real-time control of strip operation deviation according to the detection model's results. In our model, the attention module is used to improve the detection accuracy. The rolling equipment's pressing force can be real-time controlled to correct the strip running deviation. Compared with the original model, the proposed model in this paper achieves an increase in accuracy of 3 %, and the detection speed can reach 29 FPS, meeting the real-time requirements. This work can provide ideas for applying computer vision in construction of intelligent factories. • The application of computer vision in the construction of smart factories is introduced. • A new special scene object detection model. • Solved the problem of detecting strip running deviation. • A real-time control method for rolling equipment based on model's result. [ABSTRACT FROM AUTHOR]

Translating a pose of a given person to another desired pose is popular in computer vision applications. However, previous works usually directly utilized pose information to guide appearance information for the generation without deep consideration of the interaction between these two kinds of information. Moreover, the global long-range relation that exists in both kinds of data has not been well modeled due to the physical design of convolutional filters. In this paper, a novel Mutually Activated Residual Linear Modeling Generative Adversarial Network (MARLM-GAN) is proposed to address these two challenges. The MARLM-GAN consists of T cascaded MARLM modules for learning the latent transformation progressively from both appearance and pose codes. In each MARLM module, there are two mutually-activated residual linear modeling blocks for both appearance and pose pathways. In addition, an information update strategy is also developed, which makes the latent appearance and pose representations benefit each other interactively. Our experiments on two challenging datasets demonstrate that the proposed MARLM-GAN can achieve competitive results in terms of objective evaluation metrics and subjective visual realness compared with recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Real-time time-optimal trajectory planning exists in a wide range of applications such as computer numerical control (CNC) manufacturing, robotics and autonomous vehicles. Generally, the methods to generate time-optimal trajectory can be categorized as non-real-time methods and real-time methods. Non-real-time methods such as direct optimization method tend to generate time-optimal trajectory through nonlinear or linear programming while it is computationally prohibitive for high frequency real-time applications. Current real-time methods are computationally efficient but either deal with the sparse waypoint trajectories or sacrifice the time optimality a lot. This paper innovatively proposed a time-optimal switching trajectory index coordination (TOS-TIC) framework to solve the real-time time-optimal planning problem for continuous multi-axis trajectories. The proposed method is able to generate time-optimal trajectory for continuous geometric paths while considering the axial velocity and acceleration constraints. The time-optimality of the trajectory planned by TOS-TIC is nearly the same as the offline planned optimal results. Meanwhile, the proposed method is computationally efficient for even 5 kHz real-time applications. The main idea of TOS-TIC is coordinating several one-axis time-optimal switching controls to generate a modified control that decreases the state deviation from the desired trajectory. Several comparative experiments are carried out on an industrial biaxial linear motor stage. And the experimental results consistently verify that the proposed TOS-TIC real-time planner generates faster trajectory compared with the real-time lookahead method. In addition, the trajectory running time and final tracking error of the proposed method are nearly the same as the offline direct optimization method. • A novel real-time time-optimal trajectory planning method. • Systematically unites the time-optimal switching strategy and a safe control inspired multi-axis coordination. • Experiments on sinusoidal, squircle and spline curves with a linear motor stage. • The online planned trajectory is as fast as offline optimal results. [ABSTRACT FROM AUTHOR]

Cross-site scripting (XSS) attacks have been extensively studied in the literature, although mitigating such attacks remain a challenge for cyber defenders. In this paper, we survey the existing literature on XSS attacks, focusing on the range of attacks and potential mitigation strategies. Specifically, we review the various XSS attacks from the lens of an attacker. We use a workflow diagram to define the topological relationship among XSS attacks, and to highlight key system weaknesses (e.g., chokepoints). We also present a Hybrid XSS attack (H Y XSS A) , designed to facilitate the identification of existing and future potential attack vectors in different modalities presented as frameworks (f i). For quantification and visualization of these frameworks, the software application as a rotate view tool is developed. Moreover, we demonstrate how these derived frameworks can be implemented, and provide a guideline to defend against XSS attacks. The implementation results for the given two attack vector shows the feasibility of mapping of attack vectors to actual mathematical vectors. Finally, we present potential challenges and opportunities associated with XSS attacks. [ABSTRACT FROM AUTHOR]

The paper introduces novel parametric estimators for the mean value function (MVF) and variance function (VF) in geometric processes (GPs) based on a random sample which belongs to a GP. Specifically, it addresses scenarios where the distribution function of the first interarrival time of GP is analytically known, but its parameters, along with the ratio parameter of the GP, are unknown. It establishes a solid theoretical foundation for the estimators proposed and investigates some statistical properties such as strong consistency and asymptotic unbiasedness of the estimators. These results generalize some of the findings acquired in previous studies. To evaluate the performance of the proposed estimators, we conduct a comprehensive Monte Carlo simulation study. This involves using various lifetime distributions, such as gamma, Weibull, and lognormal considering different sample sizes and parameter values. A discussion of an application to software engineering is also provided to illustrate the applicability of the methods developed in this study. [ABSTRACT FROM AUTHOR]

Salient object detection (SOD) is of high significance for various computer vision applications but is a challenging task due to the complicated scenes in real-world images. Most state-of-the-art SOD methods aim to build long-range dependency for improving global contrast modeling in complicated scenes. However, most of them suffer from the prior assumption of treating image patches as visual tokens for building long-range dependency. This is because this assumption leads to localizing salient regions with uncertain boundaries due to the lost object structure information. In this paper, to address this issue, we re-construct the prior assumption of treating both patches and superpixels as visual tokens for building long-range dependency, which takes into account the properties of superpixels and patches in preserving detailed structural-aware information and local context information, respectively. Based on the re-constructed prior assumption, we propose a Collaborative Compensative Transformer Network (CCTNet) for the SOD task. CCTNet firstly alternates the computation within the same kind of vision tokens and among different vision tokens to build their dependencies. By this means, the relationship between multi-level global context and detailed structure representation can be explicitly modeled for consistent semantic and object structure understanding. Then, CCTNet performs feature joint decoding for SOD by fusing the complementary global context and detailed structure for locating objects with certain boundaries. Extensive experiments were conducted to validate the effectiveness of the proposed modules. Furthermore, the experiments on ten benchmark datasets demonstrated the state-of-the-art performance of CCTNet on both RGB and RGB-D SOD. • Re-constructing the vision prior to address complicated scenes in the SOD task. • Modeling relationships between global semantics and object structures explicitly. • Feature joint decoding for predicting salient objects. [ABSTRACT FROM AUTHOR]

Unified Modeling Language (UML) models are main artifacts of software applications and valuable assets of software projects. The similarity calculation of UML models is an effective way to model reuse, model validation, model retrieval and pattern detection, which can help developers reduce development cost. Existing studies mainly focus on UML design models such as class diagrams and object diagrams. Yet, there is little research on calculating similarity for use case models despite their importance for software requirements reuse and validation. Thus, this paper proposes a similarity calculation approach for UML use case models. This approach first transforms the use case models into use case graphs and then adopts the deep learning technique — Similarity Graph Neural Network (SimGNN) to calculate the structural similarity between use case graphs. Then it calculates the semantic similarity of use case models with Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine similarity. Finally it syntheses the results of semantic similarity and structural similarity of use case graphs. The effectiveness and efficiency of this approach was evaluated by comparing to two other approaches: (1) Cosine similarity+TF-IDF and (2) Cosine similarity+edit distance. The results showed that the our approach has better performance in terms of both effectiveness and efficiency. • A novel deep learning-based approach to calculate similarity of use case diagrams. • The integration of both semantic and structural information of use case diagrams. • A set of comparison experiments on a real dataset of use case diagrams. • Our approach is both effective and efficient. • Useful for model reuse, model validation, model retrieval and pattern detection. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce the PLEIADES framework offering a set of services and numerical tools to model and simulate the behavior of nuclear fuels of different concepts of reactors. The framework provides in particular the following features: interfaces to manipulate meshes and fields, services to deal with different physical solvers, setup of coupling trees to realize multiphysics partitioned (accelerated) fixed point couplings, automated time-marching algorithm, checkpoint/restart strategies, capability to realize on-the-fly multiscale couplings. It is built with a permanent concern for sustainability, scalability and maintainability. PLEIADES framework supports multidimensional simulations, typically 1D, 2D and 3D, possibly multilayered. To date, this framework relies on the generic thermomechanical finite elements solver Cast3M to deal with partial derivative problems (mechanical, thermal or diffusion problems) at the scale of the structure or the (heterogeneous) microstructure. It also makes use of so-called point models (mainly based on ODE — ordinary differential equations) to describe the local (mesoscale) evolution of the material through the physics of irradiation. Several software applications are built on the PLEIADES framework, they can use its parallel features to use multiple processors. The multilayered calculation scheme provides a way to loosen the computations and access to a good parallel performance up to hundred cores. It is also possible to achieve efficient scalable concurrent multiscale simulations through finite element square (FE 2) computational homogenization algorithms. • Introduction of the PLEIADES framework, dedicated to nuclear fuel behavior simulation. • Structuring and computational choices of the PLEIADES framework are described. • The multiphysics coupling is solved through an accelerated block fixed-point strategy. • Multiscale global/local coupling and computational homogenization are also available. • Parallel PLEIADES features based on MPI communications are detailed. [ABSTRACT FROM AUTHOR]

The computer vision-based analysis of railway superstructure has gained significant attention in railway engineering. This approach utilises advanced image processing and machine learning techniques to extract valuable information from visual data captured in the railway track environment. By analysing images from various sources such as cameras, drones, or sensors, computer vision algorithms can accurately detect and classify different components of the ballast superstructure, including the catenary system support, rail surface and profile, fastening system, sleeper, and ballast layer. This enables the automated assessment of the railway track's condition, stability, and maintenance needs. This paper comprehensively reviews the recent advancements, challenges, and potential applications of computer vision techniques in analysing railway superstructure. It discusses various vision-based methodologies and machine-learning approaches utilised in this context. Furthermore, it examines the benefits and limitations of computer vision-based analysis and presents future research directions for improving its applicability in railway track engineering. • Explores advanced sensing technologies and robotics. • Discusses image preprocessing, segmentation, and detection methods. • Examines track components: ballast, sleeper, fasteners, rail, and catenary system. [ABSTRACT FROM AUTHOR]

Natural Language-based Source Code Generation (NLSCG) holds the promise to revolutionize the way how software is developed by means of facilitating a collection of intelligent technical enablers, based on sustained improvements on the natural language to source code pipelines and continuous adoption of new coding paradigms. In recent years, a large variety of NLSCG technical solutions have been proposed, and quite exciting experimental results have been reported. Meanwhile, current researches and initiative application projects in this area reflect a large diversity of NLSCG contexts and of major technical enablers. Such heterogeneity, fragmentation, and vagueness of the NLSCG technical landscape are currently frustrating the full realization of the NLSCG research and application vision. Players in this field could not find systematic guidelines on how to effectively address the "known unknowns" and how to simply spot the "unknown unknowns", which eventually hinder the turning of NLSCG solutions into further research enhancements or production applications. Understanding the context, boundaries, capabilities, and integrations of NLSCG enablers is considered as one of the key drivers for the more practical application of NLSCG models. In this paper, we analyze in detail the natural language to source code pipelines and the evolvement of source code generation tasks, by considering both the problem context and technological aspects. A foresight reference framework for NLSCG is proposed to help handle the source code generation tasks with proper intelligent models. We review the present-day NLSCG technical landscape, as well as the core technical enablers along the source code generation pipelines. Relevant experiments are conducted to validate the role of representative models across different technical enablers on typical datasets, and we finally highlight the contribution of different enablers to code generation capabilities. [ABSTRACT FROM AUTHOR]

Visual tracking is one of the pre-eminent tasks in several computer vision applications. Particle filter (PF) is extensively used in visual tracking for intelligent surveillance system applications, hugely significant. But the re-sampling procedure of PF will result in sample impoverishment, which will affect the precision of tracking simultaneously. In this paper, a new tracking technique, called Trigonometric Particle Filter (TPF), based on PF optimized by Sine Cosine Algorithm (SCA), which contains trigonometric sine and cosine functions, is proposed. An enhanced method for improving the number of target particles used in a Sine Cosine Algorithm for trigonometric particle filter includes SCA ahead of the re-sampling step. This step ensures a more extensive particle set Achievement of the proposed TPF tracker is inspected and assessed on Visual Tracker Benchmark (VOT) databases. The proposed TPF tracker is compared with evolutionary-based methods like the Spider monkey optimization assisted PF (SMO-PF), Firefly algorithm-based PF (FAPF) method, Particle swarm optimization-based PF (PSO-PF) and Particle filter, recent four correlation filter-based trackers, and also with other ten state-of-the-art tracking methods. We demonstrate that visual tracking using TPF delivers additional consistent and proficient tracking outcomes than compared trackers. • A new tracking technique, called TPF, based on PF optimized by SCA, is proposed. • The SCA replaces the normal re-sampling process and improves the PF. • TPF compared with evolutionary-based approaches and 10 other leading-edge methods. • TPF visual tracking is more consistent and proficient than other trackers. [ABSTRACT FROM AUTHOR]

We are entering an era where everything is almost connected. According to Gartner, "we expect to see 20 billion internet-connected things by 2020". This evolution has engendered a colossal amount of data that traditional computer applications could no longer handle. We penetrate the epoch of smart applications: intelligent, contextual and proactive. On the other side, a trend that makes the hype is Gamification. It is now being applied to several domains especially those that targets human engagement, performance and sustainability. In this paper, through a systematic mapping study, we will present a breadth-first review of search papers that integrate gamification in Internet of Things (IoT) applications. The main aim is to provide a historical and geographic classification of research that combines IoT with gamification, the most challenging areas, and the artifacts that come out. [ABSTRACT FROM AUTHOR]

Abstract Photo-identification is a commonly used non-invasive technique that has been profitably employed in biological studies throughout the years. It starts from the assumption that a single individual can be recognized in multiple photos captured at different times by exploiting its unique representative and visible physical qualities such as marks, notches or any other definite feature. Hence, photo-identification is performed to infer knowledge about wild species' spatial and temporal distributions as well as population dynamics, thus providing valuable information especially when the species being investigated is ranked as data deficient. Furthermore, the technological improvements of the last decades and the large availability of devices with powerful computing capabilities are driving the research towards a common goal of enriching bio-ecological studies with innovative computer science approaches. In this scenario, computer vision plays a fundamental role, as it can successfully assist researchers in the analysis of large amounts of data. The aim of this paper is, in fact, to effectively provide a computer vision approach for the photo-identification of the Risso's dolphin, exploiting specific visual cues with a feature-based approach relying on SIFT and SURF feature detectors. The experiments have been conducted on image data acquired in the Gulf of Taranto from 2013 to 2017, conducting a comparative analysis of the performance of both SIFT and SURF, as well as a comparison with the state-of-the-art software DARWIN, and they proved the effectiveness of the proposed approach and suggested its application would be suitable to large scale studies. In conclusion, this paper shows an innovative computer vision application for the identification of unknown Risso's dolphin individuals that relies on a feature-based automated approach. The results suggest that the proposed approach can efficiently assist researchers during the photo-identification task of large amounts of data collected in such a challenging domain. Highlights • Computer vision is strategic for automatic photo-identification of Risso's dolphin. • SIFT based approach is robust and accurate (90%) for Risso's dolphin photo-ID. • This approach can efficiently assist researchers in large scale studies. • Increased knowledge of Risso's dolphin as data deficient species [ABSTRACT FROM AUTHOR]

Highlights • Extracting features using syntactic rules from pros/cons reviews. • Determining the opinion's polarity according to their emotion strength. • Comparing results with state-of-the-art approaches in different tasks. Abstract Sentiment knowledge extraction is a growing area of research in the literature. It helps in analyzing users’ opinions about different entities or events, which can then be utilized by analysts for various purposes. Particularly, feature-based sentiment analysis is one of the challenging research areas that analyzes users’ opinions on various features of a product or service. Of the three formats for the product reviews, our focus in this paper is limited to analyzing the pros/cons type. Due to the nature of pros/cons reviews, they are mostly concise and follow a different structure from other review types. Therefore, specialized techniques are needed to analyze these reviews and extract the customers’ discussed product features along with their personal attitudes. In this paper, we propose the Pros/Cons Sentiment Analyzer (PCSA) framework that exploits dependency relations in extracting sentiment knowledge from pros/cons reviews. We also utilize two different lexicons to ascertain the polarity strength of the extracted features based on the customers’ opinions. Several experiments are conducted to evaluate the performance of PCSA in its different phases. [ABSTRACT FROM AUTHOR]

Highlights • The criteria for the selection of green suppliers are of vital importance. • Model of Green Procurement with Coloured Petri Nets is proposed in a generic way. • Flexibility, graphical structure and synthesis of the PN model. • Future research will use fuzzy logic to apply them in the selection of suppliers. Abstract In this paper, an extension and an application to the green procurement model based on Coloured Petri Nets is presented. The large number of services and providers for services that can exist in a company means a problem when it comes to managing purchases and much more if the purchases. If the purchases have a benefit such as the sustainability of the companies processes that provide the service, the problem is even greater. This paper aims to develop an extension for this proposed model using the petri nets tools thanks to its ease of modeling complex systems. Likewise, a new algorithm applied to the place of selection of suppliers and a methodical mathematical formalization was developed. Finally, two examples are implemented. The first example allows understanding and applying the model in a simple way. In the second example or study case, be proposed an application in a services company. This example describes the step by step of the evolution of the model and the results obtained by each stage. The application of the final example shows the qualitative advantages of the developed model and the need to apply some technique of quantitative analysis of easy integration to the model that will be defining in a future research. [ABSTRACT FROM AUTHOR]

Abstract Until now, researchers have been analyzing Android applications dynamically with the use of either emulators or real devices. Emulators are an option that testers currently have to achieve scalability. Besides, these approaches can also take snapshots which help to revert back to a known state in a matter of seconds. However, emulators are often slow in performance and contain heuristic emulation traces. As for the case of real devices, restoring mechanism and hardware utilization are limitations for increasing analysis productivity. In this paper, we developed C-Android with the motivation to seek an alternative solution to solve the existing problems. C-Android leverages Linux container technology to provide restore mechanism on the bare-metal device and to utilize the most from existing hardware. To study on the opportunities of applying Android container into the analysis, we have performed several comparative implementations between C-Android and other existing approaches. The purpose of our first comparison is to assess the compatibility between container-based (C-Android) and existing solutions. The following test is related to performance between our approach and other platforms. Lastly, the utilization test is established in order to assess the possibility to optimize C-Android hardware without causing too much reduction in the performance. The challenges of applying container technology to Android analysis are also discussed in the paper as a result of our study. [ABSTRACT FROM AUTHOR]

The paper proposes a theoretical framework for using eDNA detection devices to locate missing persons, wanted criminals, and persons of interest in densely populated areas by monitoring sewer water. The proposed system includes a computer application to enter information on missing targets, and the data collected by the system can be used to narrow down their location for rescue or apprehension. The paper investigates eDNA persistence, sewer water studies, and current eDNA and DNA analysis tools to formulate a research concept. The limitations of the concept are mentioned, and it is suggested that collaboration between a large university and a leading DNA analysis equipment manufacturer is needed to custom-build eDNA detection devices to fulfill the requirements of the concept. Eventually, manufacturing costs will drive down the initial and nationwide adoption costs of the system. [ABSTRACT FROM AUTHOR]

With the development of science and technology, the computer age has come. As a new type of computer network application technology, cloud computing technology is of great significance to promote China's economic development. However, in the development of cloud technology, it faces serious security problems. Although cloud providers all claim that their services are very secure in all aspects, especially in data management. However, for large enterprises, business-related data is their lifeline. Large enterprises will not put their important applications on the public cloud for operation, and private cloud has great advantages in this respect. This paper introduces the relevant concepts and network security deployment methods of cloud technology and private cloud security platform. The research on private cloud security platform is to better resist the next generation of security threats. Deploying network security on private cloud security platform can reduce the informatization cost of enterprises, public institutions and government departments and improve work efficiency. [ABSTRACT FROM AUTHOR]

In recent years, organizations and enterprises put huge attention on their network security. The attackers were able to influence vulnerabilities for the configuration of the network through the network. Zero-day (0-day) is defined as vulnerable software or application that is either defined by the vendor or not patched by any vendor of organization. When zero-day attack is identified within the network there is no proper mechanism when observed. To mitigate challenges related to the zero-day attack, this paper presented HMM_TDL, a deep learning model for detection and prevention of attack in the cloud platform. The presented model is carried out in three phases like at first, Hidden Markov Model (HMM) is incorporated for the detection of attacks. With the derived HMM model, hyper alerts are transmitted to the database for attack prevention. In the second stage, a transductive deep learning model with k-medoids clustering is adopted for attack identification. With k-medoids clustering, soft labels are assigned for attack and data and update to the database. In the last phase, with computed HMM_TDL database is updated with computed trust value for attack prevention within the cloud. [ABSTRACT FROM AUTHOR]

In practical object detection computer vision applications, training commonly incorporates multiple data sources. Domain adaptation enhances the models' capacity to generalize across different source and target domain data. Active learning, which serves as a bridge between unsupervised and supervised domain adaptation, is an area that has been underexplored for object detection. Our methods are specifically designed to tackle the challenges of selecting target domain images for annotation, complementing the existing source domain images. First, this paper formally describes active learning in domain adaptation for object detection. We propose three new selection strategies for active learning in domain adaptation for object detection. We utilize multiple machine learning models when selecting target domain data for annotation. In particular, our best-performing selection method utilizes a domain discriminator to predict the target domain likelihood of each sample. Additionally, we introduce an evaluation framework that evaluates per-box and per-image labeling efforts in the unique object detection application. Through extensive experiments we could show that our best-performing method could improve the active learning object detection performance over random selection by about 2.4% while requiring about 13% less annotated bounding boxes on the target domain. • Integrating domain adaptation and active learning techniques for object detection. • Annotation and selection of a small target domain subset. • Using multiple machine learning models for scoring in active domain adaptation. • Our approach is effective compared to supervised and semi-supervised methods. • We propose a new per-box evaluation scheme applicable for object detection. [ABSTRACT FROM AUTHOR]

Real-world adversarial patches were shown to be successful in compromising state-of-the-art models in various computer vision applications. Most existing defenses rely on analyzing input or feature level gradients to detect the patch. However, these methods have been compromised by recent GAN-based attacks that generate naturalistic patches. In this paper, we propose a new perspective to defend against adversarial patches based on the entropy carried by the input, rather than on its saliency. We present Jedi , a new defense against adversarial patches that tackles the patch localization problem from an information theory perspective; leveraging the high entropy of adversarial patches to identify potential patch zones, and using an autoencoder to complete patch regions from high entropy kernels. Jedi achieves high-precision adversarial patch localization and removal, detecting on average 90% of adversarial patches across different benchmarks, and recovering up to 94% of successful patch attacks. Since Jedi relies on an input entropy analysis, it is model-agnostic , and can be applied to off-the-shelf models without changes to the training or inference of the models. Moreover, we propose a comprehensive qualitative analysis that investigates the cases where Jedi fails, comparatively with related methods. Interestingly, we find a significant core failure cases among the different defenses share one common property: high entropy. We think that this work offers a new perspective to understand the adversarial effect under physical-world settings. We also leverage these findings to enhance Jedi's handling of entropy outliers by introducing Adaptive Jedi, which boosts performance by up to 9% in challenging images. • An adversarial patch defense (Jedi) that uses entropy to locate and remove adversarial patches. • Jedi's precise patch localization leads to state-of-the-art performance. • Jedi is robust against adaptive entropy aware attacks as well as stealthy naturalistic patches. • A qualitative study of Jedi's failure cases suggests that a high entropy plays an important role in adversarial patches. • A new Adaptive Jedi approach to mitigate the limits. [ABSTRACT FROM AUTHOR]

This paper presents a technique using a genetic algorithm to compute an efficient routing for an application-specific NoC (Network-on-Chip). The main goal of this paper is to introduce multi-objective optimization techniques to address the NoC routing. Thus, Pareto optimization has been used to determine non-dominated solutions according to two fixed objectives: (i) avoiding the reuse of same links as far as possible to reduce congestion; (ii) reducing the number of loops to limit the risk of deadlocks. The proposed method called MORGA (Multi-Objective Routing based on Genetic Algorithm) uses two steps: (i) an off-line process consisting at selecting a non-dominated solution among a pre-calculated population of solutions; (ii) an on-line process allowing the data transmission based on the built solution by the use of routing tables. MORGA is also applicable in the presence of permanent faulty links by calculating fault-free solutions. A reconfiguration of routing tables is performed when a new application is loaded on the system. Results show how a selection of the most appropriate solution can provide considerable improvement in performance. [ABSTRACT FROM AUTHOR]

In the industrial world, there is always a problem of supply chain and always be for a long time. Production planning is one of its stages. Almost all of companies want to make production process efficient and optimized with minimum expenses while still meet with the market demand. This paper present production planning problem in furniture company with different operational constraint, including production time, warehouse capacity, and quantity of raw materials. The fuzzy goal programming applied to maximize the profit, minimize the production cost and minimize raw materials cost. In this paper was conducted at the furniture company CV. Arte Jaya which aims to help company for decision making regarding to production planning model by using fuzzy goal programming. This Method is suitable for CV. Arte Jaya because the objective function can be adapted to preferences of the company. The result obtained application program for solving decision making problem about the amount of production that is exact and still meet with the market demand. The application program that has been created is able to simplify the user’s performance of CV. Arte Jaya. [ABSTRACT FROM AUTHOR]

This paper demonstrates the usability measurement of web-based student grade processing information system. The instrument used is a USE Questionnaire to obtain user satisfaction data. Atisa Dipamkara High School has been using an online application system to process the Students Grade. The application system is a web-based information system to assist the process of making the student reports every semester. This paper discusses the method of measuring the level of use of the application of student grade processing in Atisa Dipamkara High School. One factor in measuring the quality of an information system is usability. Usability level refers to the ease of use of such information systems or software. The higher the usability value means the higher the benefits of the information system in helping the users. This measurement of usability is using USE Questionnaire consisting of 3 parameters namely benefits (usefulness), ease to use and ease to learn. Data collection involves 25 teachers as user respondents of this information system. The result of usability measurement will have the value of "feasibility" and proof that there is significant influence between usefulness variable, ease to use and ease to learn to user satisfaction variable. [ABSTRACT FROM AUTHOR]

Technological innovation is critically important for high-tech firms in laying the groundwork for corporate growth and competitive advantages. It is hoped that evaluating risk accurately and effectively will contribute to assign appropriate priorities of technological innovation projects and to enhance the success rate for the whole project. In considering the issue of risk evaluation, the essential question that arises concerns strong fuzziness, ambiguity, and inexactness during the process of assessing risks. However, comparatively little research has focused on accommodating higher degrees of uncertainty in evaluating risks associated with technological innovation projects. Because the existing risk evaluation methods (such as models using net present values, internal rates of return, fuzzy logic, real options, and stochastic simulation) have certain limitations, this paper takes the powerfulness of interval-valued Pythagorean fuzzy (IVPF) sets into account to handle imprecise and ambiguous information and to characterize complex uncertainty in practical risk evaluation problems. This paper aims to propose an IVPF inferior ratio (IR)-based assignment model and to establish a novel risk evaluation method of technological innovation for the purpose of appropriately tackling highly uncertain information in intricate and varied circumstances. The proposed method provides an effective way to fuse complicated information about risk assessments and to identify an aggregate ranking of technological innovation projects that has the closest agreement with all indicator-wise precedence ranks. The feasibility and applicability of the IVPF IR-based assignment model are illustrated via a real-world case concerning the risk evaluation of technological innovation in high-tech enterprises. Furthermore, the practicality and effectiveness of the developed methodology are verified through a comparative analysis with other relevant approaches. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a well-studied topic and one of the most challenging tasks in computer vision applications, such as autonomous driving. Deep learning approaches based on convolutional neural networks (CNN) have demonstrated exceptional success on this task in recent years. Despite this success, existing approaches are plagued by higher-order inconsistencies between the ground truth images and the ones predicted by the segmentation model. This paper proposes a novel post-processing scheme based on adversarial learning to counter these inconsistencies. Such a scheme can be combined with a variety of existing CNN-based semantic segmentation networks to improve their segmentation performances. The proposed scheme is a Two-Stream Conditional Generative Adversarial Network (TScGAN), with one stream having initial semantic segmentation masks predicted by an existing CNN, while the other stream utilizes scene images to retain high-level information under a supervised residual network structure. In addition, TScGAN incorporates a novel dynamic weighting mechanism, which leads to significant and consistent gains in segmentation performance. Several comparative tests on public benchmark driving databases, including Cityscapes, Mapillary, and Berkeley DeepDrive100K, demonstrate the effectiveness of the proposed method when used with state-of-the-art CNN-based semantic segmentation models. Furthermore, the ablation experiment proved the structural rationality of our two-stream structure. The code for TScGAN could be found at https://github.com/epan-utbm/TScGAN-for-Improving-Semantic-Predictions • Explores various post-processing techniques for enhancing semantic segmentation. • Novel Two-Stream Conditional GAN enhances urban drive scene segmentation. • Enhances feature extraction by implementing dynamic weighting within streams. • Evaluates the model against existing semantic segmentation methodologies. • Tests on Cityscapes, Mapillary, BDD100K reveal major IoU gains with proposed model. [ABSTRACT FROM AUTHOR]

Noise introduced due to weather can reduce the efficiency of computer vision applications as the visibility of the objects in images is greatly affected. Haze and rain are the most common weather conditions seen in nature. However, most of the algorithms found in the literature apply rain and haze removal approaches separately. To this end, in this paper, we propose a novel Wavelet-based deep Auto-encoder, called WAE, for simultaneously removing the haze and rain effects from images. The proposed network uses wavelet transformation and inverse wavelet transformation as an alternative to down-sampling and up-sampling operations, respectively, in order to add sparsity to the network. By training the model on both spatial and frequency domains, it learns non-stationary features that are found to be useful to remove haze and rain effects from images. The proposed model is tested on several rain and haze-affected image datasets, and it performs well in terms of standard evaluation metrics like structural similarity index measure and peak signal-to-noise ratio. The code can be found at : https://github.com/asfakali/WAE.git. • WAE, a new wavelet based Auto-Encoder for both haze and rain removal. • The network used wavelet transformation as an alternative to the upsampling and downsampling. • The model has the ability to localize features in both time and frequency domains. • The proposed model achieves state-of-the-art performance, on rain and haze datasets. [ABSTRACT FROM AUTHOR]

Interactions in the healthcare system today involve an important new set of actors: evaluative health platforms (EHPs). These platforms are not neutral intermediaries, but active moderators of how patients express opinions, choose providers, and consume health-related information. This paper adds to our understanding of the varied and evolving commercial interests of EHPs and the implications these have for patient voice. We analyze 71 platforms in the USA, UK, and Germany and identify five ideal types: subscribers, analyzers, advertisers, regulators, and scammers. Each platform type enacts a unique competitive strategy through an evaluative infrastructure which constrains but also generates possibilities for patient voice. Based on our typology, we develop three contributions. First, we nuance universalizing claims about the consequences of platform capitalism by specifying the diverse strategies underpinning competition between EHPs in different countries, and showing how each strategy leads evaluative infrastructures to develop in ways that impact patient voice. Second, we show how patients can navigate the challenges of a complex EHP space by exercising their ability to choose between platforms. Finally, we outline the conditions platforms need to fulfil to become empowering. Overall, this study highlights the varied and complex relationship between platform business models and user voice, which exists not only in healthcare, but also in many other fields. • Typology of evaluative health platforms (EHPs) in the US, the UK, and Germany. • EHP business models and content moderation techniques vary considerably. • EHPs have a wide range of implications for patient voice. • More transparency and competition can help platforms increase patient empowerment. [ABSTRACT FROM AUTHOR]

Signed Distance Fields (SDFs) have found widespread utility in collision detection applications due to their superior query efficiency and ability to represent continuous geometries. However, little attention has been paid to calculating the intersection of two arbitrary SDFs. In this paper, we propose a novel, accurate, and real-time approach for SDF-based collision detection between two solids, both represented as SDFs. Our primary strategy entails using interval calculations and the SDF gradient to guide the search for intersection points within the geometry. For arbitrary objects, we take inspiration from existing collision detection pipelines and segment the two SDFs into multiple parts with bounding volumes. Once potential collisions between two parts are identified, our method quickly computes comprehensive intersection information such as penetration depth, contact points, and contact normals. Our method is general in that it accepts both continuous and discrete SDF representations. Experiment results show that our method can detect collisions in high-precision models in real time, highlighting its potential for a wide range of applications in computer graphics and virtual reality. • The first real-time and accurate general SDF-SDF collision detection method. • A novel method for testing the intersection of analytic distance functions. • An accurate method for estimating contact information for SDF-SDF collision response stages. [ABSTRACT FROM AUTHOR]

To assess if an aggregated or an agglomerated material has to be considered as a nano-material, the size distribution of its constituent primary particles needs to be measured and the median diameter determined. To this end, the reference method uses either transmission or scanning electron microscopy to obtain images of the sample. The size of a significant number, usually a few hundreds, of primary particles are then measured manually. This task is highly time-consuming and subjected to operator bias. Some attempts have been made to automatize the size measurements. The algorithms and software available are generally successful at segmenting images of spherical objects with no or partial overlap but fail to properly segment irregular objects with strong overlap. The advances made with deep learning algorithms are promising to solve the segmentation issues encountered so far on complicated samples. In this paper, we tested the open source deep learning Cellpose software on transmission and scanning electron microscope images of different samples to retrieve the median diameter of the primary particles and compare the results with both the manual and theoretical values. This software was chosen for its ease of use, its free availability and the fact that it is pre-trained, allowing the use of a limited set of training images. For the samples used in this study, the quality of the segmentation was highly dependent on the number of objects on which the software model was trained, but a number of 500 to 1000 objects was enough to obtain good performances. The diameters measured using Cellpose segmentation are most of the time in agreement within 10% with the manual values. Interestingly, for scanning electron microscopy data, the results obtained with Cellpose are closer to the theoretical values when compared to the measurements obtained by hand, implying a smaller operator bias. If an uncertainty assessment still needs to be investigated for the diameters determined using Cellpose, this first attempt to use this software to segment electron microscope images of diverse samples is very promising and opens the possibility to fully automatize the identification of nano-structured materials. [Display omitted] • Cellpose, based on deep learning, has been applied on images of nano-aggregates. • The median diameters of the primary particles were obtained automatically. • Automatic and manual median diameters are consistent. • Only few training images are necessary to obtain good performances. [ABSTRACT FROM AUTHOR]