Reviewerly/exHarmony huggingface dataset:
Attention-based pyramid decoder network for salient object detection in remote sensing images

Salient object detection (SOD) in remote sensing images (RSIs) is a highly practical task. However, scale variations of salient objects and the diversity of salient objects in RSIs pose challenges for detection. To address these issues, an attention-based pyramid decoder network (APDNet) is proposed for SOD in RSIs. The APDNet consists of three key components. First, a multiscale attention block is constructed to extract multiscale information and relations between salient objects, suppressing the distraction of variations of object types and scales. Second, a pyramid decoder structure is designed to take full advantage of multilevel features by gradually fusing features from two adjacent layers for result prediction. This feature fusion allows APDNet to efficiently exploit multilevel feature information, thus enabling a better feature representation. Finally, a bidirectional residual refinement module is proposed to enhance the structural integrity and boundary retention of initial saliency predictions. Extensive experiments on two public datasets demonstrate the superiority and effectiveness of the proposed APDNet against other compared state-of-the-art methods.
Future Application Prospects

Driven by artificial intelligence, cloud computing, autonomous driving, the Internet of Things, blockchain, quantum computing, and other emerging technologies and applications, computing systems are becoming data-driven, flexible and self-adaptive, diversified for different requirements and personalized scenarios.
Correction of perspective distortion and intensity errors in projected fringe patterns in phase-measuring deflectometry

The relative pose between the projector and screen in a phase-measuring deflectometry (PMD) setup can result in perspective distortion and intensity errors in the projected fringe patterns. These problems degrade the sinusoidal characteristics of the fringe patterns, thus increasing phase, slope, and height errors in the PMD measurement. To overcome these issues, inverse perspective distortion and intensity modification algorithms were developed and applied to computer-generated fringe patterns before projection. The inverse perspective distortion matrices were obtained using a single image of the projected grid distortion target. The pixel intensities in the generated fringe patterns were modified using the relationship between the generated and captured pixel intensity values in grayscale images of uniform intensity. Experiments were conducted to measure the surface topographies of spherical concave and convex mirrors using a monoscopic PMD setup. Based on the experimental results, the root-mean-squared (RMS) intensity and phase errors in the projected sinusoidal fringe patterns were reduced by about 90% after using the modified fringe patterns compared to the original fringe patterns. When comparing the measured and the theoretical heightmaps, the results in PMD measurement showed an 8% and a 14% reduction of the RMS height errors in concave and convex heightmaps, respectively. The effectiveness of the algorithms in improving the accuracy of projector-based PMD measurement was demonstrated successfully.
Randomly shuffled convolution for self-supervised representation learning

Many self-supervised representation learning methods have achieved high performance in image classification tasks. However, these methods have limited performance on localization tasks such as object detection or semantic segmentation. Most self-supervised representation learning methods are optimized with only one global representation, which does not pay much attention to the spatial information in an image. We propose a simple and effective method that uses the positional relationships between the entities in an image by shuffling the convolution kernels. Our method extends current self-supervised learning and calculates the pixel-wise (dis) similarities between the output of the standard convolution kernels and that of the randomly shuffled convolution kernels. Our proposed method achieves higher performance on object detection, instance segmentation, and semantic segmentation when attached to recent self-supervised learning methods.
Programming to create machine embroidery design

The article describes the possibilities and pedagogical potential of the TurtleStitch cloud platform for introducing schoolchildren to the use of programming to create a machine embroidery design. Algorithms for several embroidery designs are given. The materials presented on the Internet about working in TurtleStitch are exclusively in foreign languages, so the content of this article will be useful for teachers teaching extracurricular activities at school, teachers of technology and informatics for an initial acquaintance with this platform. The information presented in the article can form the basis of developmental programming classes in the TurtleStitch environment from grades 2 to 11. Acquaintance of schoolchildren with the programmatic method of creating a machine embroidery design based on this article will help students expand their understanding of the areas of application of programming, develop their skills in the practical application of programming, and the ability to use information technology to solve creative problems.
Digital Transformation Success Factors Evaluation in Energy Industry

Recently, developments in technology have caused a new wave that affects the whole world. The emerging technologies have initiated the digital transformation process. Companies have made efforts to meet the digital demands and gain a share in the digital market and ensure their sustainability in the digital ecosystem. Digital transformation has become a necessity rather than an optional process. Digital is the key driver in energy industry to increase workforce efficiency and productivity, to make faster and better decisions, and to reduce costs. This study aims to evaluate the digital transformation success factors in energy industry. The success factors are obtained through a detailed literature review and experts’ opinions. Three experts are determined based on their experiences, positions in the firm, knowledge about digital transformation and its applications in the energy industry and their academic backgrounds. Fuzzy cognitive map (FCM) approach is employed to analyze the obtained digital transformation success factors with the assumption of relationship between criteria.
Feature-Enhanced Document-Level Relation Extraction in Threat Intelligence with Knowledge Distillation

Relation extraction in the threat intelligence domain plays an important role in mining the internal association between crucial threat elements and constructing a knowledge graph (KG). This study designed a novel document-level relation extraction model, FEDRE-KD, integrating additional features to take full advantage of the information in documents. The study also introduced a teacher–student model, realizing knowledge distillation, to further improve performance. Additionally, a threat intelligence ontology was constructed to standardize the entities and their relationships. To solve the problem of lack of publicly available datasets for threat intelligence, manual annotation was carried out on the documents collected from social blogs, vendor bulletins, and hacking forums. After training the model, we constructed a threat intelligence knowledge graph in Neo4j. Experimental results indicate the effectiveness of additional features and knowledge distillation. Compared to mainstream models SSAN, GAIN, and ATLOP, FEDRE-KD improved the F1score by 22.07, 20.06, and 22.38, respectively.
POLYHEDRAL COMPLETENESS OF INTERMEDIATE LOGICS: THE NERVE CRITERION

Abstract We investigate a recently devised polyhedral semantics for intermediate logics, in which formulas are interpreted in n -dimensional polyhedra. An intermediate logic is polyhedrally complete if it is complete with respect to some class of polyhedra. The first main result of this paper is a necessary and sufficient condition for the polyhedral completeness of a logic. This condition, which we call the Nerve Criterion, is expressed in terms of Alexandrov’s notion of the nerve of a poset. It affords a purely combinatorial characterisation of polyhedrally complete logics. Using the Nerve Criterion we show, easily, that there are continuum many intermediate logics that are not polyhedrally complete but which have the finite model property. We also provide, at considerable combinatorial labour, a countably infinite class of logics axiomatised by the Jankov–Fine formulas of ‘starlike trees’ all of which are polyhedrally complete. The polyhedral completeness theorem for these ‘starlike logics’ is the second main result of this paper.
Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim

With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users’ demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed.
Benchmarking equivariance for Deep Learning based optical flow estimators

The Optical Flow (OF) estimation is an important but challenging task used in various computer vision applications. Recently, the introduction of Deep Learning models has resulted in a veritable paradigm shift. However, such an approach can be biased towards the specific training data, which can lead to a considerable inaccuracy of the estimated OF in real world applications. This paper proposes a novel benchmark that can be used to identify and measure different biases of Deep Learning models for OF estimation. The benchmark focuses on testing the network equivariance, i.e. the model capability to handle data transformations. We have performed experiments based on public datasets to (1) investigate to what extent the state-of-the-art networks lack spatial equivariance when reflections are applied to the data; (2) propose new metrics and a methodology to assess the phenomenon; and (3) benchmark the state-of-the-art optical estimators and their core components for equivariance. The results show that some state-of-the-art Deep Learning techniques present a substantial degree of bias towards certain directions of motion. The proposed framework can help researchers and practitioners to develop more effective models for OF estimation. The testing and training scripts provided at https://github.com/stsavian/benchmarking_equivariance_for_of_estimators can be used to evaluate and compare different models for OF estimation.
Blackbox-based model identification of solid oxide fuel cells by hybrid Ridgelet neural network and Enhanced Fish Migration Optimizer

- An effective energy conversion tool is solid oxide fuel cell that converts chemical energy to heat and electricity by oxidant reactions and electrochemical fuel. Efficient modeling of this device can decrease the analyzing and design costs a lot. Thus, a new blackbox-based identification system is designed and defined for the solid oxide fuel cells herein. The presented method is a modified Ridgelet neural network. The method has been modified by a new enhanced design of the Fish Migration Optimizer. The new designed model, Enhanced Fish Migration Optimizer-Rigdelet neural network, is then performed on some data to train and validate. The model is finally put in comparison with several latest models to authenticate the model. Final results show that the proposed method with 1.78 MSE for 2/12/1 network topology provides the best accuracy to the others. The model is also confirmed by experimental data and the final achievements indicate that the suggested technique gives satisfying results for model identification of the solid oxide fuel cells.
Fast causal orientation learning in directed acyclic graphs

Causal relationships among a set of variables are commonly represented by a directed acyclic graph. The orientations of some edges in the causal DAG can be discovered from observational/interventional data. Further edges can be oriented by iteratively applying so-called Meek rules. Inferring edges' orientations from some previously oriented edges, which we call Causal Orientation Learning (COL), is a common problem in various causal discovery tasks. In these tasks, it is often required to solve multiple COL problems and therefore applying Meek rules could be time consuming. Motivated by Meek rules, we introduce Meek functions that can be utilized in solving COL problems. In particular, we show that these functions have some desirable properties, enabling us to speed up the process of applying Meek rules. In particular, we propose a dynamic programming (DP) based method to apply Meek functions. Moreover, based on the proposed DP method, we present a lower bound on the number of edges that can be oriented as a result of intervention. We also propose a method to check whether some oriented edges belong to a causal DAG. Experimental results show that the proposed methods can outperform previous work in several causal discovery tasks in terms of running-time.
Introduction to Machine Learning

A machine learning system works by using data to identify patterns, make decisions, and learn from them. Machine learning is based on the idea that systems can learn from data. In the traditional problem-solving approach, data is paired with a human-created program to generate answers to a problem. In machine learning, the data and answers are used to unravel the rules that create a problem. During a learning process, machines experiment with different rules and learn what works and does not work, hence the name machine learning. This chapter explores the history of machine learning, various machine learning techniques, and their comparisons.
Voice-Based Image Captioning System for Assisting Visually Impaired People Using Neural Networks

Many people worldwide have the problem of visual impairment. The authors' idea is to design a novel image captioning model for assisting the blind people by using deep learning-based architecture. Automatic understanding of the image and providing description of that image involves tasks from two complex fields: computer vision and natural language processing. The first task is to correctly identify objects along with their attributes present in the given image, and the next is to connect all the identified objects along with actions and generating the statements, which should be syntactically correct. From the real-time video, the features are extracted using a convolutional neural network (CNN), and the feature vectors are given as input to long short-term memory (LSTM) network to generate the appropriate captions in a natural language (English). The captions can then be converted into audio files, which the visually impaired people can listen. The model is tested on the two standardized image captioning datasets Flickr 8K and MSCOCO and evaluated using BLEU score.
Card-not-present fraud: using crime scripts to inform crime prevention initiatives

Abstract Growth in the online retail sector and improvements in card-present authorisation measures have led to substantial increases in card-not-present (CNP) fraud, particularly in the online retail sector. This article uses crime script analysis to understand the commission process of CNP fraud in online retail settings. Drawing upon previous crime script articles and industry reports we outline the steps involved in the three stages of CNP fraud: preparation; doing it; and getting away. From this script, points of disruption are identified and we discuss prevention measures that stakeholders such as businesses and financial institutions could implement to reduce bad actors opportunities for CNP fraud.
New Romanticism: It’s Time for New Realities

Open AccessNew Romanticism: It’s Time for New RealitiesSebastian GatzSebastian Gatz Konstfack – University of Arts, Crafts and Design in Stockholm Sweden Search for more papers by this authorhttps://doi.org/10.14220/9783737014700.55SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail AboutAbstract: The essay elaborates on the fluidity of realities and different forms of shaping them. Realities can be created and thus must be imagined first and implemented second. Reality production happens on different scales: from the small design studio – where products and desires are created – to the big world stage, where political narratives shape global happenings. At the moment we are experiencing a global dispute about howand to what extend humanity should technologically interact with nature. Should humanity intensify the control of climatic issues through the use of technology or slow down and develop new modes of being in the world? Existing movements provide space for exploring new ways of being in the world such as posthumanism, ahumanism, degrowth practice and marginalized forms of knowledge production such as occulture. Those fields come from fundamentally different (research) traditions but overlap in their quest for new non-anthropocentric ontologies. The overlapping space of these movements is conceptualized as a form of New Romanticism which opposes mainstream reality production and includes the nonhuman in its worldview. Methods for shaping realities range from speculative design to (capitalist) magic. I intend to advocate for non-hegemonic ontological explorations in academic research through experimental speculative design methods. I have written in an explorative style in order to implement speculation as amethod. I first begin by looking at a tradition of Romanticism as an “irrational” opposing force to scientism and note how tendencies towards the Romantic have crept into the impetus behind other movements. Afterwards I explore different forms of shaping realities and narratives. Then I will propose new narratives for care for the age of the Anthropocene, followed by some initial ideas of how to implement New Romanticism through methods of Speculative Design. I will conclude with a short summary. Keywords: Technology, Romanticism, Posthumanism, Degrowth, Occulture Previous chapter Next chapter FiguresReferencesRelatedDetails Download book cover 1. AuflageISBN: 978-3-8471-1470-3 eISBN: 978-3-7370-1470-0HistoryPublished online:November 2022 Information© 2022 V&R unipress, Theaterstraße 13, D-37073 Göttingen, ein Imprint der Brill-GruppeThis work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.KeywordsTechnologyRomanticismPosthumanismDegrowthOccultureAgencyWorldmakingWorldingKaren BaradDonna Harawayagential realismpolysemic linguistic tissuePDF download
Adaptive graph convolutional imputation network for environmental sensor data recovery

Environmental sensors are essential for tracking weather conditions and changing trends, thus preventing adverse effects on species and environment. Missing values are inevitable in sensor recordings due to equipment malfunctions and measurement errors. Recent representation learning methods attempt to reconstruct missing values by capturing the temporal dependencies of sensor signals as handling time series data. However, existing approaches fall short of simultaneously capturing spatio-temporal dependencies in the network and fail to explicitly model sensor relations in a data-driven manner. In this work, we propose a novel Adaptive Graph Convolutional Imputation Network for missing value imputation in environmental sensor networks. A bidirectional graph convolutional gated recurrent unit module is introduced to extract spatio-temporal features which takes full advantage of the available observations from the target sensor and its neighboring sensors to recover the missing values. In addition, we design an adaptive graph learning layer that learns a sensor network topology in an end-to-end framework, in which no prior network information is needed for capturing spatial dependencies. Extensive experiments on three real-world environmental sensor datasets (solar radiation, air quality, relative humidity) in both in-sample and out-of-sample settings demonstrate the superior performance of the proposed framework for completing missing values in the environmental sensor network, which could potentially support environmental monitoring and assessment.
Anisotropic diffusion with fuzzy-based source for binarization of degraded document images

Document image binarization plays a vital role in the document image analysis system; however, it remains challenging due to various degradations. In this paper, we propose an anisotropic diffusion model involving fuzzy-based source for binarizing degraded document images, in which the diffusion term is response for edge-preserving smoothing and the source term is used to group intensity values of foreground and background pixels into two dominant modes separated by zero. Specifically, a fuzzy classification function (FCF) is first introduced for vaguely separating foreground from background, which is defined in a local neighborhood of each point rather than in the entire image domain. Then, the fuzzy-based source is constructed by FCF and a speed restrictor, involving no threshold. In numerical aspects, we develop a parallel-serial algorithm by combining finite differencing and parallel/serial splitting methods in the literature. This algorithm is tested on seven publicly available datasets (DIBCO 2009 to 2014 and 2016) and compared with six PDE-based models and two variational models in terms of degraded document binarization. Experimental results illustrate that our model is very effective for binarization of degrade document images, and is superior to the compared models subjectively and objectively.
Nature-Inspired Optimization Algorithms: Past to Present

Nature-inspired algorithms are class of novel methods and processes for computing, analyzing, and solving various optimization problems. Nature-Inspired Optimization Algorithms (NIOAs) are bio inspired computational intelligence techniques gives an enormous drive for solving many complex problem as it exploits an exceptionally unique, strong, convincing and engaging behavior which is competent to give ideal outcomes. In the past few decades, several Nature-Inspired Optimization Algorithms has been proposed. However, very limited efforts have been made to provide a comprehensive investigation of NIOAs. In this chapter we present an overview of most significant NIOAs established from past to present days and their role in resolving complex computationally hard problems in various field of application. This overview endeavors to give a more extensive point of view and significant illumination to comprehend NIOAs. This also features the achievement, challenges and future research direction concerning recent NIOAs.