Reviewerly/exHarmony huggingface dataset:
Energy Losses Due to Imperfect Payment Infrastructure and Payment Instruments

One of the strategic objectives of the European Union is a reduction in greenhouse gas emissions and improvement of energy efficiency by at least 32.5% in different areas of the economy by 2030. However, little is known about the impact of payment in retail on energy consumption. The purpose of this paper is to assess the chain of losses of time and energy, and therefore financial losses, that occur due to the imperfection of payment infrastructure and instruments using data of cashiers’ working time. The research is based on a regression analysis method, where the energy cost per payment transaction is considered in this study as a function of the number of customers per hour and the energy cost. The results of the panel models highlight that the number of customers per hour has a negative impact on the cost of energy per payment transaction. Furthermore, modern means and methods of payment, including cryptocurrencies, do not solve the problem of the excessive time that it takes to service payments, which entails a waste of energy and money. The empirical results give valuable insights into how to best organise payment in retail to achieve lower energy costs and improve energy efficiency in payment infrastructure.
Refleksi Hasil PISA (The Programme For International Student Assesment): Upaya Perbaikan Bertumpu Pada Pendidikan Anak Usia Dini)

The results of the program for international student assessment (PISA) in 2018 in the category of reading ability, Indonesia ranked 74th out of 79 countries, while for the assessment of mathematical ability and scientific ability, Indonesia ranked 73rd and 71st out of 79 participating countries PISA Achievement of Indonesia's ranking in the PISA assessment has always been constant since the beginning of Indonesia's participation in the assessment from 2000 to 2018. With consistent results that are ranked below bring the consequence of thinking that the quality of Indonesian education is not in accordance with global community standards and below the countries other in the world. Government efforts to make improvements to the results of the PISA assessment are changes in the curriculum, but in reality the results of PISA have not experienced significant changes. This paper is an effort to provide a view on the improvement of PISA results through improving the quality of early childhood education, among others for several reasons, first, the opinions of experts based on the results of their research which found that the development of intellectual abilities reached 80% at an early age so the term which is often used is the golden age (golden age); second, all indicators in the program for international student assessment (PISA) begin to be studied and developed by children in early childhood education institutions ranging from literacy reading (language), mathematical literacy and scientific literacy to be the focus in early childhood learning in institutions PAUD is holistically integrative; third, PAUD access which has not yet covered the whole territory of the Republic of Indonesia, as well as the quality and quality of early childhood education institutions that have not been maximized.
Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques

Breast cancer has evolved as the most lethal illness impacting women all over the globe. Breast cancer may be detected early, which reduces mortality and increases the chances of a full recovery. Researchers all around the world are working on breast cancer screening tools based on medical imaging. Deep learning approaches have piqued the attention of many in the medical imaging field due to their rapid growth. In this research, mammography pictures were utilized to detect breast cancer. We have used four mammography imaging datasets with a similar number of 1145 normal, benign, and malignant pictures using various deep CNN (Inception V4, ResNet-164, VGG-11, and DenseNet121) models as base classifiers. The proposed technique employs an ensemble approach in which the Gompertz function is used to build fuzzy rankings of the base classification techniques, and the decision scores of the base models are adaptively combined to construct final predictions. The proposed fuzzy ensemble techniques outperform each individual transfer learning methodology as well as multiple advanced ensemble strategies (Weighted Average, Sugeno Integral) with reference to prediction and accuracy. The suggested Inception V4 ensemble model with fuzzy rank based Gompertz function has a 99.32% accuracy rate. We believe that the suggested approach will be of tremendous value to healthcare practitioners in identifying breast cancer patients early on, perhaps leading to an immediate diagnosis.
MCSA: Multi-strategy boosted chameleon-inspired optimization algorithm for engineering applications

Chameleon swarm algorithm (CSA) is a newly proposed swarm intelligence algorithm inspired by the chameleon’s foraging strategies of tracking, searching and attacking targets, and has shown well competitive performance with other state-of-the-art algorithms. Interestingly, CSA mathematically models and implements the steps of chameleon’s unique food-seeking behavior. Nevertheless, the original CSA suffers from the challenges of insufficient exploitation ability, ease of falling into local optima, and low convergence accuracy in complex large-scale applications. Aiming at these challenges, an efficient enhanced chameleon swarm algorithm termed MCSA, combined with fractional-order calculus, sinusoidal adjustment of parameters and crossover-based comprehensive learning (CCL) strategy, is developed in this paper. Firstly, a good fractional-order calculus strategy is added to update the chameleon’s attack velocity, which heightens the local search ability of CSA and accelerates the convergence speed of the algorithm; meanwhile, the sinusoidal adjustment of parameters is adopted to provide a better balance between exploration and exploitation of CSA. Secondly, the CCL strategy is used for the mutation to increase the diversity of the population and avoid becoming trapped in local optima. Three strategies enhance the overall performance and efficiency of the native CSA. Finally, the superiority of the presented MCSA is verified in detail by comparing it with native CSA and several state-of-the-art algorithms on the well-known 23 benchmark test functions, CEC2017 and CEC2019 test suites, respectively. Furthermore, the practicability of MCSA is also highlighted by six real-world engineering designs and two truss topology optimization problems. Simulation results demonstrate that MCSA has strong competitive capabilities and promising prospects. MCSA is potentially an excellent meta-heuristic algorithm for solving engineering optimization problems.
Detail Preserved Single Image Dehazing Algorithm Based on Airlight Refinement

Single-image haze removal is important for many practical applications (e.g., surveillance). However, dehazed results of existing algorithms tend to be oversmoothed with missing fine image details. This drawback is caused by two factors: inaccurate airlight estimations and disregarding multiple scattering. In this paper, we propose a detail-preserving image dehazing algorithm based on two key priors, namely, the depth-edge aware prior and the airlight impact regularity prior. The proposed algorithm makes contributions in both the haze removal step and the postprocessing step. First, based on the depth-edge aware prior, an airlight refinement algorithm is proposed. The gradient strength of the minimum channel is employed to calculate punishment weights to smooth the dark channel. Second, based on the airlight impact regularity prior, an adaptive sharpening model that considers the refined airlight to determine the sharpening strength value is established to enhance levels of detail. Experimental results demonstrate that the proposed algorithm cannot only effectively remove haze but can also enhance levels of detail to thus outperform the state of the art on a wide variety of images.
Transfer subspace learning via low-rank and discriminative reconstruction matrix

In this paper, we investigate the unsupervised domain transfer learning in which there is no label in the target samples whereas the source samples are all labeled. We use the transformation matrix to transfer both target and source samples to a common subspace where they have the same distribution and each target sample in the transformed space is constructed of a linear combination of the source samples. To preserve the local and global structure of the samples in the transferred domain, the low-rank and sparse constraints are imposed on the reconstruction coefficient matrix. In this paper, in order to consider the discriminative ability of the target and source samples, the information content of the reconstruction coefficient matrix is utilized. To capture the discriminative ability of the target samples, it is assumed that the class labels of the source samples which are linearly incorporated in constructing a target sample should be the same. Based on this assumption, it is assured that the target samples are well distributed over the transferred domain. To handle this, we utilize the linear entropy to measure the discriminant power of the target domain. This term considers the discriminative ability of the target samples without using their hidden labels. Also, to assess the discriminative ability of source samples, we use max-margin classifier where the kernel matrix is defined by using the reconstruction coefficient matrix. To evaluate the proposed approach, it is applied on MSRC, VOC 2007, CMU PIE, Office, Caltech-256, Extended Yale B and two imbalanced datasets. The experimental results show that our proposed approach outperforms its competitors.
ADMOST: UAV Flight Data Anomaly Detection and Mitigation via Online Subspace Tracking

Since the control laws require sensor feedback to set the current dynamic state of the unmanned aerial vehicle (UAV), incorrect readings may lead to potentially catastrophic conditions. Thus, automated detection and mitigation of UAV flight data anomaly is an important problem in the aviation domain. However, the conventional anomaly detection algorithms simply detect the outlier points and cannot provide estimated values. To address this challenge, anomaly detection and mitigation algorithm based on online subspace tracking, an online algorithm for flight data anomaly detection and mitigation is proposed. At every time instant, data subspace matrix is used as a meaningful data representation of raw multivariate heterogeneous flight data. Besides, in terms of outlier points, the subspace matrix is tracked with incomplete partial observation. Anomaly score is calculated based on the identification of a change in the underlying subspace. Utilizing the tracked subspace matrix, the detected outlier points are replaced with reasonable recovered estimations, which will help mitigate the influence of anomaly to UAV system control. Experimental results on real UAV flight data demonstrate its ability to maintain high accuracy for anomaly detection and low error for data recovery.
Kernel Distance Metric Learning Using Pairwise Constraints for Person Re-Identification

Person re-identification is a fundamental task in many computer vision and image understanding systems. Due to appearance variations from different camera views, person re-identification still poses an important challenge. In the literature, KISSME has already been introduced as an effective distance metric learning method using pairwise constraints to improve the re-identification performance. Computationally, it only requires two inverse covariance matrix estimations. However, the linear transformation induced by KISSME is not powerful enough for more complex problems. We show that KISSME can be kernelized, resulting in a nonlinear transformation, which is suitable for many real-world applications. Moreover, the proposed kernel method can be used for learning distance metrics from structured objects without having a vectorial representation. The effectiveness of our method is validated on five publicly available data sets. To further apply the proposed kernel method efficiently when data are collected sequentially, we introduce a fast incremental version that learns a dissimilarity function in the feature space without estimating the inverse covariance matrices. The experiments show that the latter variant can obtain competitive results in a computationally efficient manner.
Statistical Nearest Neighbors for Image Denoising

Non-local-means image denoising is based on processing a set of neighbors for a given reference patch. few nearest neighbors (NN) can be used to limit the computational burden of the algorithm. Resorting to a toy problem, we show analytically that sampling neighbors with the NN approach introduces a bias in the denoised patch. We propose a different neighbors' collection criterion to alleviate this issue, which we name statistical NN (SNN). Our approach outperforms the traditional one in case of both white and colored noise: fewer SNNs can be used to generate images of superior quality, at a lower computational cost. A detailed investigation of our toy problem explains the differences between NN and SNN from a grounded point of view. The intuition behind SNN is quite general, and it leads to image quality improvement also in the case of bilateral filtering. The MATLAB code to replicate the results presented in the paper is freely available.
A Surrogate-Assisted Multiobjective Evolutionary Algorithm for Large-Scale Task-Oriented Pattern Mining

As a branch of frequent pattern mining, the task-oriented pattern mining has received increasing attention due to its broad application scenarios. The lexicographic subset tree based algorithm and the multiobjective evolutionary algorithm are two effective approaches for finding the most frequent and complete pattern in task-oriented applications. However, both suffer from heavy computational cost since their runtime increases rapidly as the transaction dataset is scaled up. To address this issue, this paper regards the task-oriented pattern mining as a data-driven optimization problem and solves it by using a surrogate-assisted multiobjective evolutionary algorithm. Based on the framework of our previous multiobjective evolutionary algorithm for task-oriented pattern mining, the proposed algorithm estimates the objective values of most solutions using an ensemble of surrogates instead of the real objective functions, thereby highly improving the efficiency of the algorithm. Experimental results on three task-oriented applications indicate that the proposed algorithm has better efficiency than state-of-the-art algorithms.
Perceived argument quality's effect on threat and coping appraisals in fear appeals: An experiment and exploration of realism check heuristics

Persuasion is key to encourage compliance with information security policies through fear appeals, though research has not examined how the perceived quality of their arguments affects threat and coping appraisals. Because we know that perceived argument quality can influence attitudes and behavior, it may improve fear appeal effectiveness. The results of a scenario-based field experiment suggest that perceived argument quality increases response efficacy perceptions and compliance intentions. We also examine emerging heuristics about how to use realism checks in scenario-based research and find that current realism check heuristics in behavioral information security research may be misguided, contributing to biased interpretation.
Unsupervised Deep Learning by Neighbourhood Discovery

Deep convolutional neural networks (CNNs) have demonstrated remarkable success in computer vision by supervisedly learning strong visual feature representations. However, training CNNs relies heavily on the availability of exhaustive training data annotations, limiting significantly their deployment and scalability in many application scenarios. In this work, we introduce a generic unsupervised deep learning approach to training deep models without the need for any manual label supervision. Specifically, we progressively discover sample anchored/centred neighbourhoods to reason and learn the underlying class decision boundaries iteratively and accumulatively. Every single neighbourhood is specially formulated so that all the member samples can share the same unseen class labels at high probability for facilitating the extraction of class discriminative feature representations during training. Experiments on image classification show the performance advantages of the proposed method over the state-of-the-art unsupervised learning models on six benchmarks including both coarse-grained and fine-grained object image categorisation.
Translating expressions of intrusion-related mineral systems into mappable spatial proxies for mineral potential mapping: Case studies from the Southern New England Orogen, Australia

An understanding of the modelled mineral system and high-quality data that accurately map this system are prerequisites for producing geologically meaningful mineral potential maps. Critical to this is the translation of the targeted mineral system components into mappable targeting criteria and their spatial proxies. This paper presents a workflow that illustrates this translation process, also highlighting that, if done well, mineral potential mapping can produce statistically valid, geologically meaningful, and practically useful results that not only predict the location of known mineralisation but also identify new target areas. An important ingredient of the workflow described herein is what we call a mineral systems atlas. This compendium includes (1) a spatial data table detailing the translation of the modelled mineral system, (2) the predictive maps that capture the mappable components of the targeted mineral system, and (3) the final mineral potential maps. A case study implementing and illustrating this workflow is presented for intrusion-related Au and Sn ± W mineral systems in the Southern New England Orogen, New South Wales, Australia. Importantly, the mineral potential maps generated as part of this study succeeded in identifying areas of known intrusion-related Au and Sn ± W mineralisation and new areas with high potential for discovery.
The role of meaning in attentional guidance during free viewing of real-world scenes

In real-world vision, humans prioritize the most relevant visual information at the expense of other information via attentional selection. The current study sought to understand the role of semantic features and image features on attentional selection during free viewing of real-world scenes. We compared the ability of meaning maps generated from ratings of isolated, context-free image patches and saliency maps generated from the Graph-Based Visual Saliency model to predict the spatial distribution of attention in scenes as measured by eye movements. Additionally, we introduce new contextualized meaning maps in which scene patches were rated based upon how informative or recognizable they were in the context of the scene from which they derived. We found that both context-free and contextualized meaning explained significantly more of the overall variance in the spatial distribution of attention than image salience. Furthermore, meaning explained early attention to a significantly greater extent than image salience, contrary to predictions of the ‘saliency first’ hypothesis. Finally, both context-free and contextualized meaning predicted attention equivalently. These results support theories in which meaning plays a dominant role in attentional guidance during free viewing of real-world scenes.
Research Progress Of Quantum Repeaters

Abstract At present, the main components of quantum communication channels are optical fibers and free space. However, whether it is a fiber channel or a free-space channel, the channel in which it propagates is affected by noise, resulting in loss or absorption of photons. Experimental studies have shown that the quantum key can basically meet the requirements of the local area network, and the safety distance is about 100km. However, if you want to achieve longer distance distribution, you can’t do without quantum repeaters. The quantum repeater can divide the long-distance communication distance into several segments, and then perform quantum key distribution in several segments respectively; then establish a longer-distance EPR pair between adjacent nodes, and establish an EPR pair to utilize entanglement switching and Entanglement purification technology; finally repeat the above steps, and finally achieve long-distance information transmission. The development of quantum repeaters can solve the communication problems facing the world and promote the replacement of global communication methods.
Joint person re-identification and camera network topology inference in multiple cameras

In this study, we propose a unified framework which jointly solves both person re-identification and camera network topology inference problems with minimal prior knowledge about the environments. The proposed framework takes general multi-camera network environments into account and can be applied to online person re-identification in large-scale multi-camera networks. In addition, to show the superiority of the proposed framework, we provide a new person re-identification dataset with full annotations, named SLP, captured in the multi-camera network. Experimental results using our re-identification and public datasets show that the proposed methods are promising for both person re-identification and camera topology inference tasks.
Self-Supervised Generalisation with Meta Auxiliary Learning

Learning with auxiliary tasks can improve the ability of a primary task to generalise. However, this comes at the cost of manually labelling auxiliary data. We propose a new method which automatically learns appropriate labels for an auxiliary task, such that any supervised learning task can be improved without requiring access to any further data. The approach is to train two neural networks: a label-generation network to predict the auxiliary labels, and a multi-task network to train the primary task alongside the auxiliary task. The loss for the label-generation network incorporates the loss of the multi-task network, and so this interaction between the two networks can be seen as a form of meta learning with a double gradient. We show that our proposed method, Meta AuXiliary Learning (MAXL), outperforms single-task learning on 7 image datasets, without requiring any additional data. We also show that MAXL outperforms several other baselines for generating auxiliary labels, and is even competitive when compared with human-defined auxiliary labels. The self-supervised nature of our method leads to a promising new direction towards automated generalisation. Source code can be found at \url{https://github.com/lorenmt/maxl}.