Reviewerly/exHarmony huggingface dataset:
The effect of governance on entrepreneurship: from all income economies perspective

Abstract The purpose of this study is to analyze the effect of governance indicators on Entrepreneurship. Explanatory research design with Pearson correlation and multiple linear regression models were applied. Five-year World Bank data (2014–2018) of 126 countries from all economic development levels were used. Worldwide governance indicators considered are voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and corruption control. Gross net income was taken as a control variable. To measure entrepreneurship, the number of formally registered limited liability businesses as a percentage of the working-age population, was used. To make highly skewed time series data of dependent variable (entrepreneurship) closer to normal, logarithmic transformation was made and heteroscedasticity of residuals was checked. The finding of Pearson correlation shows that there are moderate to strong significant correlations (0.466 ≤ r ≤ 0.806, p &lt; 0.01) between predictors and the outcome variable. Predictor variables have moderate to very strong correlation among each other (0.506 ≤ r ≤ 0.956, p &lt; 0.01). Regression analysis was computed after two highly collinear variables were dropped from the model using the variance inflation factor (VIF) test. The study found that the remaining four independent variables and the control variable predict 71.5% of the variance in the outcome variable. Except for voice and accountability, all predictors have their own statistically significant influence on entrepreneurship. Thus, working on each predictor up to the standard application can bring incremental changes in new business formation and entry. The researchers believe that this study is of significant interest to policymakers, program developers, entrepreneurs, analysis, and supporters, since it provides useful insight on how governance indicators influence entrepreneurship.
Comparing Attack Models for IT Systems: Lockheed Martin’s Cyber Kill Chain, MITRE ATT&amp;CK Framework and Diamond Model

Cyberattacks are a serious challenge for any IT system, which motivates cybersecurity professionals and developers to constantly explore various attack models to analyse the cyberattack lifecycle used by attackers to mount their attacks. Several attack models have been proposed and successfully utilised to analyse the various types of cyberattacks and their mitigations. All the models offer different characteristics, advantages and disadvantages. Therefore, it is important to examine the most popular attack models to understand their characteristics and suitability for modelling specific types of attacks. This paper will examine characteristics, advantages and disadvantages of the three most popular attack models: the Lockheed Martin’s Cyber Kill Chain, the MITRE ATT&CK Framework and the Diamond Model. Finally, it will provide a comparative study of these three attack models to present a relative summary for selecting the most suitable attack model for a specific application.
Knowledge aggregation networks for class incremental learning

Most existing class incremental learning methods rely on storing old exemplars to avoid catastrophic forgetting. However, these methods inevitably face the gradient conflict problem, the inherent conflict between new streaming knowledge and existing knowledge in the gradient direction. To alleviate gradient conflict, this paper reuses the previous knowledge and expands the branch to accommodate new concepts instead of fine-tuning the original models. Specifically, this paper designs a novel dual-branch network called Knowledge Aggregation Networks. The previously trained model is frozen as a branch to retain existing knowledge, and a consistent trainable network is constructed as the other branch to learn new concepts. An adaptive feature fusion module is adopted to dynamically balance the two branches’ information during training. Moreover, a model compression stage maintains the dual-branch structure. Extensive experiments on CIFAR-100, ImageNet-Sub, and ImageNet show that our method significantly outperforms the other methods and effectively balances stability and plasticity.
Optical image encryption based on linear canonical transform with sparse representation

We propose optical image encryption and authentication in the linear canonical transform (LCT) method by using a sparse representation technique. The linearity in the LCT based double random phase encryption (DRPE) system caused the vulnerability of the system. Not only the linearity in the LCT -DRPE system causes the vulnerability of the system but also the LCT order parameter may cause this weakness. Therefore, the encrypted data of double random phase encoding based on LCT is integrated with sparse representation to overcome this weakness. Unlike the traditional DRPE method, only sparse data from the encrypted data is kept for decryption. The numerical simulation proved that the correct authentication results are satisfied despite the use of partially encrypted data. The randomly selective sparse encrypted data reinforces the security of amplitude encoding of DRPE-LCT. Thus, the proposed method added an additional layer to optical security systems. The attacks analysis is implemented in order to show the robustness of the proposed security. In addition, this method has fulfilled the transmission and storage requirements owing to the high reduction of information data.
RDERL: Reliable deep ensemble reinforcement learning-based recommender system

Recommender systems (RSs) have been employed for many real-world applications including search engines, social networks, and information retrieval systems as powerful intelligent techniques for creating personalized recommendations. RSs mainly utilize different data resources such as user-item ratings, tag data, and social relationships in their recommendation process to overcome the data sparsity issue. One of the main challenges is how to integrate the results obtained by these data resources. Moreover, most of the existing RSs focus on the recommendation accuracy, and do not consider the reliability of the recommendations. To address these challenges, in this study, a reliable recommendation method is developed, which employs deep neural networks and reinforcement learning. The proposed method firstly uses a stacked denoising autoencoder model to obtain the latent features from the rating, tag, and trust data. The similarity value between each pair of users is then calculated in three different aspects according to the extracted latent features. An ensemble approach is developed by integrating the three similarities using reinforcement learning to determine the final similarity value. The ensemble similarity values are used in the rating prediction process, and the reliability of the predicted ratings is evaluated using an effective measure. Finally, a recommendation strategy is developed based on the integration of the predicted ratings and their reliability values. We evaluated the effectiveness of the proposed method by conducting numerous experiments on three datasets where the results show that the proposed method is far superior to other recommendation models in making more accurate recommendations.
M-Sequences and Sliding Window Based Audio Watermarking Robust Against Large-Scale Cropping Attacks

Large-scale cropping (LSC) is one of the mostly-used operations in desynchronization attacks and can easily destroy the watermark information by deleting continuous audio slices from the watermarked audio. In this work, we propose a spread spectrum (SS) based audio watermarking scheme to resist against LSC attacks more robustly from both theoretical and empirical perspectives. Specifically, we first perform discrete wavelet transform (DWT), graph-based transform (GBT), and singular value decomposition (SVD) on the host audio signal to produce transform coefficients. Next, we embed the chaotic encrypted watermark into DWT-GBT-SVD coefficients by the SS technique. Then, we combine m-sequences with the encrypted watermark to generate the watermarking key, which can theoretically guarantee the self-restoration of the cropped watermark based on the periodicity of m-sequences. Additionally, we develop an effective sliding window (SW) strategy to extract the fragmentary watermark slices from DWT-GBT-SVD coefficients and restore the integral watermark by the watermarking key. Finally, the proposed audio watermarking scheme, named m-SW-LSC, is compared with the state-of-the-art audio watermarking methods on audio signals with different genres and lengths under various attacks with different ratios. Experimental results demonstrate that our m-SW-LSC has a superior performance in restoring the complete watermark and exhibits significantly high robustness against LSC attacks.
TS-REPLICA: A novel replica placement algorithm based on the entropy weight TOPSIS method in spark for multimedia data analysis

Performance optimization based on node attributes is of profound significance in the replica placement algorithm used in Hadoop distributed file system (HDFS). Currently, most researchers studying replica placement algorithms consider only a single attribute or a few multiple attributes. However, a single attribute cannot accurately express the performance of a node. Therefore, this paper proposes a replica placement algorithm based on the entropy weight TOPSIS (technique for order preference by similarity to ideal solution) method, called TS-REPLICA. First, a multi-attribute matrix that comprehensively reflects the performance and load of nodes is defined. Then, a TOPSIS-based algorithm is proposed to calculate the performance score of each data node. In addition, the entropy weight method is introduced to derive the weights of attributes for balancing the influence of weights of multiple attributes. Next, the comprehensive load score of each data node in the Spark cluster, the average comprehensive load score of each rack, and the average comprehensive load score of the entire cluster are calculated, and replica placement is performed based on the obtained scores. Finally, the effectiveness of the proposed algorithm is verified on various datasets and test cases. The experimental results show that the TS-REPLICA algorithm outperforms the better comparison algorithm in execution number in Spark cluster.
Mel Spectrogram-based advanced deep temporal clustering model with unsupervised data for fault diagnosis

Fault diagnosis of mechanical equipment using data-driven machine learning methods has been developed recently as a promising technique for improving the reliability of industrial systems. However, these methods suffer from data sparsity due to the difficulty in data collection, which limits the feature extraction of anomalies. To solve this problem, we propose the mel spectrogram-based advanced deep temporal clustering (ADTC) model, which can extract and verify the features of unlabeled data through an unsupervised learning based autoencoder and the K-means. In addition, the ADTC model uses the proposed centroid based learning to obtain calibrated unsupervised learning data by minimizing the data point and target centroid distances for misclustered encoder output features in ensemble-based unsupervised learning. The classifier of the ADTC model uses a supervised learning based deep support vector machine network model, which is robust to nonlinear data, to diagnose the faults of the mechanical equipment. The proposed ADTC model was validated using mechanical equipment dataset with data augmentation to address the imbalanced dataset problem. During experiments, the mel spectrogram-based ADTC model exhibited the best performance in the various industrial environment with a prediction accuracy as high as 98.06%, outperforming other compared algorithms.
Learning visual question answering on controlled semantic noisy labels

Visual Question Answering (VQA) has made great progress recently due to the increasing ability to understand and encode multi-modal inputs based on deep learning. However, existing VQA models are usually based on assumptions of clean labels, and it is contradictory to real scenarios where labels are expensive and inevitably contain noises. In this paper, we take the lead in addressing this issue by establishing the first benchmark of controlled semantic noisy labels for VQA task, evaluating existing methods, and coming up with corresponding solutions. Specifically, through analyzing human labels of existing VQA datasets, we first design a controlled semantic label noise by imitating human mislabeling behavior, which is more reasonable than conventional random noise. Then, we evaluate several popular VQA models on these new benchmark datasets and show that their performance degrades significantly compared to the original setting. To this end, we propose a Semantic Noisy Label Correction (SNLC) to mitigate impacts of noisy labels, including a Semantic Cross-Entropy (SCE) loss and a Semantic Embedding Contrastive (SEC) loss. Extensive experiments demonstrate the effectiveness of the proposed method SNLC. The proposed approach achieves a stable improvement on several existing models. The source code is available at https://github.com/zchoi/SNLC.
A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification

Breast ultrasound (BUS) imaging is commonly used in the early detection of breast cancer as a portable, valuable, and widely available diagnosis tool. Automated BUS image classification and segmentation can assist radiologists in making accurate and fast decisions. Recent studies illustrate that tumor, peritumoral, and background regions of BUS images provide valuable information for BUS image segmentation or classification. However, few studies have investigated the influence of these three regions on multi-task learning. In this study, we propose an RMTL-Net to simultaneously segment tumor regions and classify tumors in BUS images into benign or malignant categories. To improve both segmentation and classification performance, we design a regional attention (RA) module that employs the predicted probability maps to automatically guide the classifier to learn important category-sensitive information in the tumor, peritumoral, and background regions and seamlessly fuse them to obtain a better feature representation. We conduct detailed ablation experiments of the proposed RA module and comparative experiments with four recent state-of-the-art peer multi-task learning methods, three single-task segmentation methods, and four single-task classification methods on two public BUS datasets. Experimental results show that the proposed RMTL-Net achieves the best overall segmentation and classification accuracy in terms of five segmentation metrics and six classification metrics.
SABV-Depth: A biologically inspired deep learning network for monocular depth estimation

Monocular depth estimation makes it possible for machines to perceive the real world. The prediction performance of the depth estimation network based on deep learning will be affected due to the depth of the deep network and the locality of convolution operations. The imitation of the biological visual system and its functional structure is becoming a research hotspot. In this paper, we study the interpretability relationship between the biological visual system and the monocular depth estimation network. By concretizing the attention mechanism in biological vision, we propose a monocular depth estimation network based on the self-attention mechanism, named SABV-Depth, which can improve prediction accuracy. Inspired by the biological visual interaction mechanism, we focus on the information transfer between each module of the network and improve the information retention ability, and enable the network to output a depth map with rich object information and detailed information. Further, a decoder module with an inner-connection is proposed to recover depth maps with sharp edge contours. Our method is experimentally validated on the KITTI dataset and NYU Depth V2 dataset. The results show that compared with other works, the proposed method improves prediction accuracy. Meanwhile, the depth map has more object information and detail information, and a better edge information processing effect.
Internet of Things and Blockchain Technologies in the Insurance Sector

With cutting-edge technologies like Blockchain technology, the Internet of Things (IoT), and Artificial Intelligence, the insurance industry is entering a new era of advancements. Financial innovations are widely affecting economic activities. IoT and blockchain technologies are considerably aiding the expansion of the insurance industry in this new era. This article examines the impact of IoT and blockchain technologies on the insurance industry and describes the phenomena from several angles. Digital insurers are growing more popular now, but it is essential to understand the problems and challenges they face, which result in poor penetration. Although there are many advantages for the insured and insurer, internet insurance providers are subject to operational, regulatory, and company reputation. Customers worry about the safety of their transactions and identity theft. This article aims to illustrate the fundamentals and the significance of IoT and blockchain technologies in the insurance business, looking at the benefits that accrue to the insurance provider and the customer. The findings of the study observed that Blockchain technology helps reduce insurers’ administrative costs of insurers and ensure the insured person’s claim adjustment. On the other hand, the Internet of Things helps to share more information about the clients and mitigate risk.
Simulated and Real Robotic Reach, Grasp, and Pick-and-Place Using Combined Reinforcement Learning and Traditional Controls

The majority of robots in factories today are operated with conventional control strategies that require individual programming on a task-by-task basis, with no margin for error. As an alternative to the rudimentary operation planning and task-programming techniques, machine learning has shown significant promise for higher-level task planning, with the development of reinforcement learning (RL)-based control strategies. This paper reviews the implementation of combined traditional and RL control for simulated and real environments to validate the RL approach for standard industrial tasks such as reach, grasp, and pick-and-place. The goal of this research is to bring intelligence to robotic control so that robotic operations can be completed without precisely defining the environment, constraints, and the action plan. The results from this approach provide optimistic preliminary data on the application of RL to real-world robotics.
Reliability modeling and contrastive learning for unsupervised person re-identification

Unsupervised person re-identification (ReID) aims to learn discriminative identity features in scenarios without a ground-truth. Fully unsupervised person ReID methods usually iterate between pseudo-labels prediction and representation learning and have achieved promising performance. However, these methods are usually hampered by noisy labels. To address this issue, we propose a reliability modeling and contrastive learning (RMCL) method, which aims to reduce the impact of noisy labels and enhance the robustness of the model to hard samples. On the basis of existing work, first, we define the concept of probabilistic stability and design a stability estimation scheme to improve pseudo-labels reliability modeling. Second, we explore the reliability–informativity function to redefine the weights of samples, which can be easily introduced to existing optimization methods. Finally, we expand the range of hard samples and design an identity hard contrastive loss, which is used to increase the robustness of the model to hard samples. Experiments on three large-scale person ReID datasets (Market-1501, DukeMTMC-reID and MSMT17) validate the effectiveness of RMCL, which surpasses state-of-the-art fully unsupervised methods and cross-domain methods. Furthermore, we explore the superiority of RMCL in a new “three2one” scenario.
Quantum tomography using state-preparation unitaries

We describe algorithms to obtain an approximate classical description of a d-dimensional quantum state when given access to a unitary (and its inverse) that prepares it. For pure states we characterize the query complexity for ℓq-norm error up to logarithmic factors. As a special case, we show that it takes applications of the unitaries to obtain an ε-ℓ2-approximation of the state.For mixed states we consider a similar model, where the unitary prepares a purification of the state. We characterize the query complexity for obtaining Schatten q-norm estimates of a rank-r mixed state, up to polylogarithmic factors. In particular, we show that a trace-norm (q = 1) estimate can be obtained with queries. This improves (assuming our stronger input model) the ε-dependence over the works of O'Donnell and Wright (STOC 2016) and Haah et al. (IEEE Trans. Inf. Theory, 63.9, 2017), that use a joint measurement on copies of the state.To our knowledge, the most sample-efficient results for pure-state tomography come from setting the rank to 1 in generic mixed-state tomography algorithms, which can require a large amount of computing resources. We describe sample-optimal algorithms for pure states that are simple and fast to implement.Along the way we show that an ℓ∞-norm estimate of a normalized vector induces a (slightly worse) ℓq-norm estimate for that vector, without losing a dimension-dependent factor in the precision. We also develop an unbiased and symmetric version of phase estimation, where the probability distribution of the estimate is centered around the true value. Finally, we give an efficient method for estimating multiple expectation values, improving over the recent result by Huggins et al. (arXiv:2111.09283) when the measurement operators do not fully overlap. More specifically, we show that for E1,…, Em normalized measurement operators, all expectation values Tr(Ejρ) can be efficiently learned up to error ε with applications of a state-preparation unitary for a purification of ρ.* The full version of the paper can be accessed at https://arxiv.org/abs/2207.08800
A joint multiobjective optimization of feature selection and classifier design for high-dimensional data classification

Feature selection (FS) in data mining and machine learning has attracted extensive attention. The purpose of FS in a classification task is to find the optimal subset of features from given candidate features. Recently, more and more meta-heuristic algorithms have been used to deal with the FS problems. However, meta-heuristic algorithms suffer from certain issues, such as large search space for solutions and huge time consumption. Moreover, most of existing meta-heuristic algorithms focus only on the selection of an optimal feature subset, and pay little attention to the optimal design of the classifier. In this article, we propose a joint multiobjective optimization method for both feature selection and classifier design, called JMO-FSCD. The proposed approach uses neural network as a classifier and introduces a non-iterative algorithm for training the classifier so as to ensure good performance and fast learning. A new coding scheme is also designed for optimizing FS and classifier simultaneously. For demonstrating the superiority of the proposed approach, its performance is compared with those of six state-of-the-art FS algorithms. Experimental results on thirty-five benchmark data sets reflect the superior performance of the proposed JMO-FSCD.