Reviewerly/exHarmony huggingface dataset:
Research on optimization and application of Spark decision tree algorithm under cloud‐edge collaboration

This paper optimizes and parallelizes the C4.5 algorithm on the Spark platform under cloud-edge collaboration. First, the information entropy calculation in the definition of boundary points and Fayyad's theorem is improved by the Gini index, which reduces the number of information entropy calculations for selecting segmentation points in the traditional C4.5 algorithm continuous attribute discretization operation and simplifies the calculation formula, thus reducing the execution time of the algorithm. On this basis, the CFS algorithm is introduced to optimize the calculation of the information gain ratio, to facilitate the selection of better attributes for decision tree partitioning. The improved C4.5 algorithm is then parallelized in Spark platform. In this paper, we choose the “intelligent door guard” of cloud-edge collaboration under the epidemic prevention and control management context as the application scenario for experimental verification and make timely risk assessment for those who want to enter the place and give them a response whether they are allowed to enter or not. Experiments show that the improved parallel C4.5 algorithm reduces the running time and raises the accuracy of the algorithm, compared with the traditional C4.5 algorithm and previous improvements to it. In the application scenario of cloud-edge collaboration, the improved algorithm in this paper decreases the time delay and increases the inspection efficiency.
Absolute phase retrieval using one coding pattern for the dynamic 3-D measurement

This paper proposes an absolute phase retrieval using one additional coding pattern to measure the complex object. Compared with traditional temporal phase unwrapping (TPU) methods, the proposed coding pattern is generated from phase-shifting patterns and the phase difference rather than stair stripes containing different quantified gray-level. In this paper, N-step phase-shifting profilometry (PSP) can provides 2 N codewords, which can be used to label the large number of fringe orders. To ensure measuring accuracy, the errors closed to turning points can be identified by quantified phase difference, and then compensated. Experimental results demonstrate that the proposed method can achieve the same level of measuring accuracy as the classical PSP, and has strong robustness on the measurement of the object with sharp and colorful surfaces. The dynamic experiment shows that the proposed method only requiring one additional pattern can achieve the 3-D reconstructed rate of 30 fps.
Synthetic Individual Income Tax Data: Promises and Challenges

Tax data are invaluable for research, but privacy concerns severely limit access. Although the US Internal Revenue Service produces a public-use file (PUF), improved technology and the proliferation of individual data have made it increasingly difficult to protect. Synthetic data are an alternative that reproduce the statistical properties of administrative data without revealing individual taxpayer information. This paper evaluates the quality and safety of the first fully synthetic PUF and demonstrates its performance in tax model microsimulations. The synthetic PUF could also be used to develop and debug statistical programs that could then be safely run on confidential data via a validation server.
Wavefront Aberration Measurement Deflectometry for Imaging Lens Tests

Lenses play an important role in imaging systems. Having an effective way to test the aberrations of imaging lenses is important. However, the existing methods cannot satisfy the requirements in some conditions. To overcome these difficulties, wavefront aberration measurement deflectometry (WAMD) is proposed in this paper, which can reconstruct the wavefront aberrations of imaging lenses by measuring the angular aberrations. The principle of WAMD is analyzed in detail, and the correctness and feasibility of the proposed method are verified by both a simulation and an experiment. A telephoto lens and a single imaging lens were tested in an experiment, and the RMS errors were 166.8 nm (5.71%) and 58.9 nm (4.74%), respectively, as compared with the interferometer’s results. This method is widely applicable with relatively reasonable accuracy. It has potential to be applied in the lens manufacturing and alignment process.
A Conceptual Model of Factors Influencing Customer Relationship Management in Global Software Development: A Client Perspective

The software development industry or organizations increasingly emerging day by day have adopted global software development (GSD) practices due to the large significance of outsourcing. These industries face many challenges due to a lack of understanding customer perspective in the GSD environment. For any organization, the customer is the major stakeholder, and customer relationship management (CRM) plays a vital role in customer satisfaction with software development projects. These challenges create serious risks for any software development project’s success. Thus, CRM is a crucial challenge in the success of software projects in the GSD environment. This research study aims to address the factors that negatively influence CRM implementation in the global context and proposes a conceptual model based on the identified factors for enhancing software product quality. The systematic literature review (SLR) phase investigates the potential barriers to CRM implementation in GSD. Based on identified barriers, an initial conceptual model is developed. The proposed conceptual model is validated using a questionnaire survey of the GSD industry and CRM practitioners of Pakistan. Statistical analysis and several suitable tests are also performed to develop the final conceptual model for CRM implementation in the GSD environment. This research is performed from the client’s perspective. The results are promising and accommodating to avoid any software project failure due to customer-related issues in a GSD environment.
Mammographic Classification of Breast Cancer Microcalcifications through Extreme Gradient Boosting

In this paper, we proposed an effective and efficient approach to the classification of breast cancer microcalcifications and evaluated the mathematical model for calcification on mammography with a large medical dataset. We employed several semi-automatic segmentation algorithms to extract 51 calcification features from mammograms, including morphologic and textural features. We adopted extreme gradient boosting (XGBoost) to classify microcalcifications. Then, we compared other machine learning techniques, including k-nearest neighbor (kNN), adaboostM1, decision tree, random decision forest (RDF), and gradient boosting decision tree (GBDT), with XGBoost. XGBoost showed the highest accuracy (90.24%) for classifying microcalcifications, and kNN demonstrated the lowest accuracy. This result demonstrates that it is essential for the classification of microcalcification to use the feature engineering method for the selection of the best composition of features. One of the contributions of this study is to present the best composition of features for efficient classification of breast cancers. This paper finds a way to select the best discriminative features as a collection to improve the accuracy. This study showed the highest accuracy (90.24%) for classifying microcalcifications with AUC = 0.89. Moreover, we highlighted the performance of various features from the dataset and found ideal parameters for classifying microcalcifications. Furthermore, we found that the XGBoost model is suitable both in theory and practice for the classification of calcifications on mammography.
A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression

Depression is a serious illness that affects millions each year and if left untreated, it may lead to the deaths of many.It comes in many flavors that can be very different among people and this makes diagnosing it very difficult.A lot of artificial intelligence systems have been designed to diagnosing depression but they failed to perform feature selection and extraction on the dataset used in training the systems and this has a huge implication on the classification accuracy of the system.The objective of this research work is to develop a depression diagnosis system, that takes into consideration feature selection and extraction of dataset using Genetic-Neuro-Fuzzy techniques.Feature selection and extraction, will enable identification of key symptoms and hidden traits which are vital in diagnosis of depression.In this work, a Genetic Neuro-Fuzzy Model which is capable of handling feature selection and extraction on depression dataset was proposed and designed for diagnosing clinical depression.The GA component optimizes the clinical dataset which consist of series of diagnosed depression cases by performing feature selection and extraction, while ANFIS is used in training the optimized dataset obtained from the GA.The system had 92.5% prediction accuracy.This is a significant improvement over the best related model in literature that achieved a prediction accuracy of 92.4%.The system is recommended for psychiatrist hospital to aid in depression diagnosis.The research is limited to the diagnosis of clinical depression; future work should focus on the other forms of depression and treatments.The model has incorporated feature selection and feature extraction for the prediction of clinical depression with significant results established with performance indicators.
Improved lattice enumeration algorithms by primal and dual reordering methods

The security of lattice-based cryptosystems is generally based on the hardness of the Shortest Vector Problem (SVP). The original enumeration (ENUM) algorithm solving SVP runs in exponential time due to the exhaustive search, which is used as a subroutine for the block Korkin–Zolotarev (BKZ) algorithm. It is a critical issue to reduce the computational complexity of ENUM. In this paper, first, we improve the reordering method proposed by Wang et al. in ACISP 2018. We call our proposed method DPR, which permutates the projected dual lattice vectors by decreasing norms. Preliminary experimental results show that the proposed reordering methods can reduce the ENUM complexity compared to the predecessor; for instance, DPR reduces around 32.8% on average in 45-dimensional lattices. Moreover, the authors’ simulation shows that the higher the lattice dimension, the more DPR can reduce the ENUM complexity. In addition, we study a condition for deciding when the reordering method shall be executed or not. Finally, we improve the BKZ algorithm with DPR methods and the proposed condition.
Get off of Chain: Unveiling Dark Web Using Multilayer Bitcoin Address Clustering

Bitcoin is the most widely used cryptocurrency for illegal trade in current darknet markets. Owing to the anonymity of its addresses, even though transaction flows are globally visible, Bitcoin clustering remains one of the most challenging and open problems in illegal Bitcoin transaction analysis. In this article, to resolve this problem, we propose a novel <i>multi-layer heuristic</i> algorithm for Bitcoin clustering, which leverages on-chain transactions as well as off-chain application data in the real world. For this purpose, we first explored the unique characteristics of darknet market ecosystems including their trading systems. By conducting an in-depth analysis of the data manually collected for 11 months, we found that some darknet market review data disclosed transactions containing Bitcoin value and item delivery information. We then identified unique Bitcoin addresses associated with the disclosed information, owned by the same darknet providers. Based on address ownership, more accurate market clusters could be created, which have not previously been identified by other clustering algorithms. According to our experimental results, approximately 31.68&#x0025; of the darknet market review data matched real Bitcoin transactions, and 122 hidden clusters associated with Silk Road 4 were found. This indicates that the proposed algorithm can complement existing clustering methods and significantly reduce the false negative rate by up to 91.7&#x0025;.
Deep-Learning-Based Side-Channel Analysis of Block Cipher PIPO With Bitslice Implementation

Recently, as deep learning has been applied to various fields, deep-learning-based side-channel analysis (SCA) has been widely investigated.Unlike traditional SCA, it can perform well independently of the attacker's ability.In this paper, we propose deep-learning-based profiled and non-profiled SCA of PIPO, (Plug-In Plug-Out), which is a bitslice block cipher that can effectively apply a countermeasure for SCA.Our datasets were captured from three different boards (XMEGA128D4, MSP430F2618, STM32F303) running PIPO-64/128.For profiled SCA, the identity (ID) labeling method exhibited better performance than the most significant bit (MSB) and hamming weight (HW) labeling methods.That is, even if each bit of the S-Box output was distributed in the power traces by the bitslice implementation, the neural network trained well each bit of the S-Box output by itself.For non-profiled SCA, we proposed a novel labeling technique that considers bitslice characteristics.We compared our proposed labeling method to MSB and HW labeling by analyzing the three aforementioned datasets.For non-profiled SCA, the proposed labeling method was more effective than the MSB and HW labeling methods on all datasets.
Stereo super-resolution images detection based on multi-scale feature extraction and hierarchical feature fusion

Recently, with most mobile phones coming with dual cameras, stereo image super-resolution is becoming increasingly popular in phones and other modern acquisition devices, leading stereo super-resolution images spread widely on the Internet. However, current image forensics methods are carried out in monocular images, and high false positive rate appears when detecting stereo super-resolution images by these methods. Therefore, it is important to develop stereo super-resolution image detection method. In this paper, a convolutional neural network with multi-scale feature extraction and hierarchical feature fusion is proposed to detect the stereo super-resolution images. Multi-atrous convolutions are employed to extract multi-scale features and be adapt for varying stereo super-resolution images, and hierarchical feature fusion further improve the performance and robustness of the model. Experimental results demonstrate that the proposed network can detect stereo super-resolution images effectively and achieve strong generalization and robustness. To the best of our knowledge, it is the first attempt to investigate the performance of current forensics methods when tested under stereo super-resolution images, and represent the first study of stereo super-resolution images detection. We believe that it can raise the awareness about the security of stereo super-resolution images.
Exploring the Semi-Supervised Video Object Segmentation Problem from a Cyclic Perspective

Modern video object segmentation (VOS) algorithms have achieved remarkably high performance in a sequential processing order, while most of currently prevailing pipelines still show some obvious inadequacy like accumulative error, unknown robustness or lack of proper interpretation tools. In this paper, we place the semi-supervised video object segmentation problem into a cyclic workflow and find the defects above can be collectively addressed via the inherent cyclic property of semi-supervised VOS systems. Firstly, a cyclic mechanism incorporated to the standard sequential flow can produce more consistent representations for pixel-wise correspondance. Relying on the accurate reference mask in the starting frame, we show that the error propagation problem can be mitigated. Next, a simple gradient correction module, which naturally extends the offline cyclic pipeline to an online manner, can highlight the high-frequent and detailed part of results to further improve the segmentation quality while keeping feasible computation cost. Meanwhile such correction can protect the network from severe performance degration resulted from interference signals. Finally we develop cycle effective receptive field (cycle-ERF) based on gradient correction process to provide a new perspective into analyzing object-specific regions of interests. We conduct comprehensive comparison and detailed analysis on challenging benchmarks of DAVIS16, DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is helpful to enhance segmentation quality, improve the robustness of VOS systems, and further provide qualitative comparison and interpretation on how different VOS algorithms work. The code of this project can be found at https://github.com/lyxok1/STM-Training .
Perceptions of the &lt;i&gt;Book Creator&lt;/i&gt;’s role as a forefront of SEL during the COVID-19 pandemic

This study analyzes the experience of pre-teacher students using the Book Creator (BC)–a digital tools, during emergency remote education of COVID-19. The inductive analysis of the interviews aims to answer the question: What competences of social-emotional learning (SEL) were manifested from the students’ perspective as a result of the implementation of the BC while conducting emergency remote education? This inductive analysis exposed three main themes that are attributed to SEL as it is described in the literature review. The first theme–a collaborative space for peer learning such as learning with the BC. The second theme–moral dilemmas affiliated with a collaborative learning space. The third theme–continuous multi-media feedback that was made possible because of the various means of expression possible with the BC. The results of this study indicate that prudent use of technology can aid in teaching processes, teaching and evaluating students experiencing emergency-remote-education as part of their individual and group experience of learning skills that are relevant for SEL.
Seemingly unrelated clusterwise linear regression for contaminated data

Abstract Clusterwise regression is an approach to regression analysis based on finite mixtures which is generally employed when sample observations come from a population composed of several unknown sub-populations. Whenever the response is continuous, Gaussian clusterwise linear regression models are usually employed. Such models have been recently robustified with respect to the possible presence of mild outliers in the sub-populations. However, in some fields of research, especially in the modelling of multivariate economic data or data from the social sciences, there may be prior information on the specific covariates to be considered in the linear term employed in the prediction of a certain response. As a consequence, covariates may not be the same for all responses. Thus, a novel class of multivariate Gaussian linear clusterwise regression models is proposed. This class provides an extension to mixture-based regression analysis for modelling multivariate and correlated responses in the presence of mild outliers that let the researcher free to use a different vector of covariates for each response. Details about the model identification and maximum likelihood estimation via an expectation-conditional maximisation algorithm are given. The performance of the new models is studied by simulation in comparison with other clusterwise linear regression models. A comparative evaluation of their effectiveness and usefulness is provided through the analysis of a real dataset.
PoS4MPC: Automated Security Policy Synthesis for Secure Multi-party Computation

Abstract Secure multi-party computation (MPC) is a promising technique for privacy-persevering applications. A number of MPC frameworks have been proposed to reduce the burden of designing customized protocols, allowing non-experts to quickly develop and deploy MPC applications. To improve performance, recent MPC frameworks allow users to declare variables secret only for these which are to be protected. However, in practice, it is usually highly non-trivial for non-experts to specify secret variables: declaring too many degrades the performance while declaring too less compromises privacy. To address this problem, in this work we propose an automated security policy synthesis approach to declare as few secret variables as possible but without compromising security. Our approach is a synergistic integration of type inference and symbolic reasoning. The former is able to quickly infer a sound—but sometimes conservative—security policy, whereas the latter allows to identify secret variables in a security policy that can be declassified in a precise manner. Moreover, the results from symbolic reasoning are fed back to type inference to refine the security types even further. We implement our approach in a new tool PoS4MPC . Experimental results on five typical MPC applications confirm the efficacy of our approach.
Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

In order to solve the problems of low brightness contrast of a color image, hiding a large amount of detail information, and deviation of color information in the process of image acquisition, an optimization method of plane image color enhancement processing based on computer vision virtual reality is proposed. In this method, the input RGB image is converted into the image represented by the HSI color model, and its adaptive brightness is adjusted to improve the overall brightness of the image. For the local detail enhancement of the color image, the three-dimensional Gaussian model perceived by retinal neurons is introduced into the illuminance image estimation of the MSR algorithm to enhance the image color. The results are as follows: from the perspective of objective parameter evaluation, the mean, standard deviation, information entropy, and average gradient of example images 1 and 2 are improved by about 70%; this algorithm not only enhances the brightness and contrast of the image but also maintains the detailed edge information of the image and the color characteristics of the object itself. The average enhancement rate is the highest among various algorithms, up to 95%. The algorithm proposed in this paper maintains the edge detail information of the image, optimizes the defects of the combination of traditional bilateral filtering and Retinex algorithm, and the color is also well restored, which makes the monitoring image easier to identify, more conducive to criminal investigation and solving cases, and lays a foundation for subsequent image processing.