Reviewerly/exHarmony huggingface dataset:
Predicting factors influencing survival of breast cancer patients using logistic regression of machine learning

Breast cancer is the most common cancer in women worldwide. majority of studies focused on identifying factors that have an effect on survival using statistical methods, but few studies have used machine learning techniques. This study aimed to use machine learning to develop a Logistic Regression Model for identifying the factors that influence the survival of patients with breast cancer. Dataset gained from patients' records at the Department of Oncology (DoO), the Benghazi Medical Centre during a period from 2018 to 2019. The dataset contained 9 predictors and class attributes (survival). The prediction model was built using logistic regression. Our study showed that both the training dataset and the testing dataset have achieved above 94% as a performance of the logistic regression model. The important factors identified in this study were cancer stage classification, tumour size, number of positive lymph nodes, and types of primary treatment. Also, odds ratio results showed that "Mastectomy" as the treatment, Grade II, "positive nodes of 1 to 3", and "tumour size, 2 cm or less" increase survival. Our study concluded that the logistic regression classifier showed high accuracy hence machine learning methods could be used as alternative predictive tools in breast cancer survival studies.
A Semi-Supervised Railway Foreign Object Detection Method Based on GAN

The rapid development of deep learning provides new technical means for railway foreign object detection. However, in practical applications, the datasets of railways with foreign objects are scarce. In order to solve this problem, by improving the loss function and anomaly image evaluation standard, this paper proposes a new semi-supervised anomaly detection method based on GAN (Generative Adversarial Networks). Experiments show that our method can achieve railway foreign object detection without anomaly prior knowledge. Regarding anomaly recognition, a 0.058 AUC (Area Under Curve) and a 6% classification accuracy relative improvement for the railway dataset used in this paper are obtained.
SkillsLab+ - Augmented Reality Enhanced Medical Training

The digitization of medical studies can help future doctors cope with new opportunities and challenges of our time. Computer-assisted procedures have long since become part of a doctor's daily routine and the use of modern digital technology is unlikely to decline in the future. In order to be better prepared, the use of modern technology must already take place during studies and training. This is exactly where SkillsLab+ comes in and combines the first practical experiences of the medical course SkillsLab with state of the art methods of augmented reality. Additional information, images and instructions are projected directly into the learner's field of vision and they learn how to use this new technology in a meaningful way. Supporting digitization of the SkillsLab can promote self-study and helps students to be better prepared for the actual teaching. Upcoming studies will investigate this assumption and a comparison between the conventional SkillsLab and the enhanced and and digitised SkillsLab+ will be sought.
On Detecting Growing-Up Behaviors of Malicious Accounts in Privacy-Centric Mobile Social Networks

Privacy-centric mobile social network (PC-MSN), which allows users to build intimate and private social circles, is an increasingly popular type of online social networks (OSNs). Because of strict usage policy enforced by PC-MSNs (such as restricted account and content access), malicious accounts (or users) have to act like normal accounts to accumulate credentials before committing malicious activities. Therefore, analysis merely relying on static account profile information or social graphs is ineffective to detect such growing-up accounts. Besides, existing behavior-based malicious account detection methods fail to effectively detect growing-up accounts who pretend to be benign and have similar behaviors to benign users during the growing-up stage.
Exploring National Library of Scotland datasets with Jupyter Notebooks

The National Library of Scotland’s Digital Scholarship Service has been releasing collections as data on its data-delivery platform, the Data Foundry, since September 2019. Following the COVID-19 lockdown, this service experienced significantly higher traffic, as library users increasingly made use of online resources. To ensure that as many users as possible were able to explore the datasets on the Data Foundry, the Library invested in a Digital Research Intern post, with a remit to provide introductory analysis of the Data Foundry collections using Jupyter Notebooks. This article provides a case study of this project, explaining the Library’s work to date around its new Digital Scholarship Service and releasing datasets on the Data Foundry; the reasoning behind the decision to begin to provide Jupyter Notebooks; the Notebooks themselves and what types of analysis they contain, as well as the challenges faced in creating them; and the publication and impact of the Notebooks.
Guided Image Weathering using Image-to-Image Translation

In this paper, we present a guided image weathering method that allows the user to generate the weathering process. The core of our method is a three-step method to generate textures at different time steps of the weathering process. The input texture is analyzed first to obtain the weathering degree (age map) for each pixel, then we train a conditional adversarial network to generate texture patches with diverse weathering effects. Once the training is finished, new weathering results can be generated by manipulating the age map, such as automatic interpolation and manually modified by the user.
Near-field photometric stereo using a ring-light imaging device

Traditional photometric stereo reconstructs the 3D surface shape of the target in fine details but assumes distant/directional lighting and orthogonal imaging. Yet, systematic errors occur in practical applications where the lights are divergent and near to the object. In this paper, we present a new post-processing method to correct the bias using a ring-light imaging device. We demonstrate and put forward a bias regulation process in ring-light equipment using traditional photometric stereo, where an approximate linear deviation exists in the surface gradient and a related quadratic deviation exists in the estimated surface height. Based on this low-frequency bias regulation, the proposed method corrects the systematic errors in traditional photometric stereo by using a set of sparse known real 3D points of the object surface, in order to fit the quadratic height error model and obtain more accurate 3D heights estimation. The proposed method has been evaluated using both simulated and real data to verify its accuracy and effectiveness, and has achieved significant performance. In addition, we integrated ring-light sources in homemade equipment to conduct real-world experiments, which demonstrates significant bias reduction and higher reconstruction quality compared to the existing methods. The source code of our method is available at https://github.com/fanhao/ring-light-photometric-stereo .
Adaptation of the meCUE 2.0 Version for User Experience(UX) Measurement Approach into Indonesian Context

The user experience (UX) of an item must be evaluated by assessing its user experience as a key feature of product growth. There are several frameworks for user experience assessment questionnaires, one of which is very popular: meCUE. However, the meCUE framework was originally developed in German, then in English, and no research has yet been conducted to develop the Indonesian version of the meCUE framework. This study aims to adapt the meCUE 2.0 framework into the Indonesian version using cross-cultural adaptation and reliability testing. The meCUE 2.0 framework is a user experience questionnaire consisting of 33 questions detailed in Modules I and II, on Perception of instrumental and non-instrumental product qualities, Module III on Emotions, and Module IV on Consequences. This adaptation version is then tested against the Smart Regency Service Application, namely Pamekasan Smart Mobile Application (PSMA), involving 15 respondents from technical and non-technical backgrounds who will be given facial validity and 60 respondents to verify the validity of the Indonesian version of meCUE 2.0 for the various populations. The test results of Cronbach's Alpha from the adaptation version in Indonesian for the meCUE 2.0 framework are 0.868 for module I, 0.870 for module II, 0.894 for module III, and 0.841 for module IV, which concludes that this version can be relied on for use by user experience practitioners. This adaptation version is expected to help researchers and user experience practitioners in Indonesia evaluate product user experiences.
Texture enhanced Statistical Region Merging with application to automatic knee bones segmentation from CT

Statistical Region Merging technique belongs to the portfolio of very successful image segmentation methods across diverse domains and applications. The method is based on a solid probabilistic principle and was extended in various directions to suit specific applications, including those from medical domains. In its basic implementation the technique is based on a merging criterion relying on image pixel intensities. Sufficient to segment well some natural scene images, it often deteriorates dramatically when challenging medical images are segmented. In this study we introduce a new merging criterion into the method which utilizes texture characteristic of the image. We demonstrate that the enhanced criterion allows segmentation of knee bones in CT comparable to state-of-the-art outcomes found in literature while preserving the desirable properties of the original technique.
Unsupervised visual feature learning based on similarity guidance

The availability of a large amount of image data and the impracticality of annotating each sample, coupled with various changes in the target class, such as lighting, posture, etc., make the performance of feature learning disappointing on unlabeled datasets. Lack of attention to hard sample pairs in network modeling and one-sided consideration of similarity measurement in the process of merging have exacerbated the huge performance gap between supervised and unsupervised feature expression. In order to alleviate these problems, we propose an unsupervised network that gradually optimizes feature expression under the guidance of similarity. It employs the deep network to train high-dimensional features and small-scale merge to generate high-quality labels to alternately execute the two steps. Feature learning is guided by gradually generating high-quality labels, thereby narrowing the huge gap between unsupervised learning and supervised learning. The proposed method has been evaluated on both general datasets and the datasets for person re-identification (person re-ID) with superior performance in comparison with existing state-of-the-art methods.
Comparative Study and Secure Data Deduplication techniques for Cloud Computing storage

Cloud based communication is a tremendous growth in the world and which provides the services through virtualized resources with support of internet. In cloud computing, optimized storage techniques require to store the hugea mount of data. Performing the data deduplication technique over the encrypted data consider as the major challenging task. Eliminating the redundant data file from the optimized storage, which results the minimizing the band width and reduces the cost and disk usage. In Existing approach, the most of the research work are using Attribute Based Encryption to prevent the data security. However, the security protection issues during the Attribute Based Encryption are considered as challenging task. In the traditional method of cloud data storage, the data are usually encrypted in the server side and then securely stored in remote server. Many researchers proposed many algorithms in which it makes easier for user's convenience so that it makes them fulfil their requirements. In, this proposed work detailed comparative study for data deduplication techniques in cloud storage are analysed. The results indicate that the cloud computing allows the users to perform the limited outsourcing performance of computational task with extraordinary server. Our proposed deduplication scheme enhances to improve a secured connection for attribute-based encryption for an emerging source to use and it proved the secured against the application system.
Multi-surface phase-shifting algorithm using the window function fitted by the nonlinear least squares method

A multi-surface phase demodulation algorithm is proposed to measure surfaces of the transparent plate simultaneously. A non-linear least-squares method based on the trust region is utilized to fit the window function derived from the characteristic polynomials. The fitted window function requires fewer parameters and has a flexible form, which means the window length can be stretched freely and used flexibly. Then, a multi-surface phase demodulation technique combining the discrete Fourier principle and the fitted window function is investigated, allowing simultaneous reconstruction of the measured front surface, rear surface, and thickness variation. During the implementation of our algorithm, the processing time for the interferograms with 850×850 pixels is around 5.1s. Measurement errors caused by several factors are analyzed, including wavelength-tuning error, frequency order error, cavity length error, and average thickness error. The experimental result on a rectangular transparent plate further verifies the effectiveness of our algorithm.
Zipf’s laws of meaning in Catalan

In his pioneering research, G. K. Zipf formulated a couple of statistical laws on the relationship between the frequency of a word with its number of meanings: the law of meaning distribution, relating the frequency of a word and its frequency rank, and the meaning-frequency law, relating the frequency of a word with its number of meanings. Although these laws were formulated more than half a century ago, they have been only investigated in a few languages. Here we present the first study of these laws in Catalan. We verify these laws in Catalan via the relationship among their exponents and that of the rank-frequency law. We present a new protocol for the analysis of these Zipfian laws that can be extended to other languages. We report the first evidence of two marked regimes for these laws in written language and speech, paralleling the two regimes in Zipf's rank-frequency law in large multi-author corpora discovered in early 2000s. Finally, the implications of these two regimes will be discussed.
Accurate Breast Cancer Detection and Classification by Machine Learning Approach

In this paper there is comparison of four different machine learning algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Fuzzy logic and Genetic algorithm on Wisconsin Breast Cancer Diagnosis (WBCD) dataset for the detection of breast cancer in women. The test accuracies are compared to show the efficient algorithm for the detection of breast cancer using those algorithms. The dataset is partitioned to 70% training data and 30% testing data. The results for the applied algorithms are CNN acquired 96.49% accuracy, RNN acquired 63.15% accuracy, fuzzy logic acquired 88.81% accuracy, and genetic algorithm acquired 80.399% accuracy.
Track prediction algorithm based on GA-BPNN

A track prediction algorithm is proposed based on the Genetic Algorithm (GA) and Back Propagation neural network (BPNN), in which the GA is used to optimize the initial weights and thresholds of hidden layers of the network. Firstly, a 5-5-4 topology of the BPNN for track prediction is established according to the characteristics of track prediction. Its inputs are historical tracks, and the output just is the predicted track. Secondly, the weights and thresholds of the BPNN are regarded as genes of individual in GA to construct chromosomes, individuals, and populations. Then, the optimal solution of the GA is used as the weights and thresholds of the BPNN for network training to get a better output. In the experiment, the track data are derived from Automatic Dependent Surveillance-Broadcast, and experimental results show that the proposed algorithm can improve both the accuracy and time efficiency of the BPNN. In addition, we use the predicted track of the proposed method to detect flight conflict by constructing conflict scenarios. The detecting process and results are visualized in the Geographic Information System, and the correct results of detecting again show that the proposed method is reliable.
Algorithms and Optimization Techniques for Solving TSP

The traveling salesman problem (TSP) is one of the most extensively studied optimization problems in the computer science and computational mathematics field given that there is yet an optimal solution for it to be discovered. This algorithmic issue requests the shortest possible route that visits each city precisely once and returns to its initial starting point if a list of n places and the distances between each pair are given. This paper conducts a comparative study to test and evaluate the performance of three algorithms: Simulated Annealing, Ant Colony Optimization, and Genetic Algorithm. With the traveling salesman problem classifying under NP-hard computational complexity, the proposed research work will examine the runtime as well as the shortest distance computed by each of these algorithms by setting up analogous environments of n cities.
Monte Carlo WLS Fuser for Nonlinear/Non-Gaussian State Estimation

Over the last few decades there has been an explosion of work in the area of nonlinear/non-Gaussian state estimation. Probably the best known methods to solve this problem is extended Kalman filter, unscented Kalman filter, quadrature Kalman filter and particle filter, etc. The majority of algorithms are based on the framework of the Bayesian filter and limited to the additive Gaussian noise. Motivated by these reasons, this paper proposes a novel approach which fuses two kinds of independent state information from the prediction and the observation, respectively. The approach, named the Monte Carlo weighted least squares (WLS) fuser, is not depended on the prediction-correction processes and can be applied to the nonadditive non-Gaussian scenario. The simulation demonstrates the feasibility of the Monte Carlo WLS Fuser and compares the performance of the proposed approach with some cutting-edge methods.
Particle Filter for Joint Carrier Phase, Doppler Shift and Doppler Rate Estimation and Data Detection

This paper proposes a novel method for joint estimation of carrier phase, Doppler shift, Doppler rate, and data detection using particle filter (PF). Using sequential importance sampling (SIS) the proposed algorithm estimates the posterior joint probability of unknown parameters and transmitted data symbols in a recursive fashion. We also propose a method for fine tuning of particles for quick convergence. The Monte Carlo simulations confirm the validity of the proposed algorithm. We show numerically that the bit error rate (BER) performance of the proposed particle filter with the fine tuning method is within 0.5-1 dB of the ideal scenario (no error in parameter estimation) with only 100 particles. Simulations with fine tuning of particles depict significant gains in signal-to-noise ratio (SNR) for root-mean-square error (RMSE) of Doppler shift and Doppler rate (compared to cases without fine tuning of particles).
A Face-Mask Detection System Based on Deep Learning Convolutional Neural Networks

The alarming situation caused by COVID-19 has prompted the artificial intelligence community to think about using new tools in order to contribute to the fight against COVID-19. Computer vision is a sub-discipline of AI that is blossoming since 2010s and helps in diverse fields like robotics, self-driving cars, health care by handling very complex nonlinear problems. In the wake of this, its contribution to the fight against COVID-19 has to be taken into account. In fact, only a few papers have been published recently in that regard despite the seriousness of the situation. This is what fueled our desire to make a contribution. This paper is intended to elaborate very powerful and robust mask detection model enabling the detection of not only individuals wearing the recommended masks from those who don’t, but the detection of a third category of people not wearing it appropriately is also taken into account by our model.
Comparative Judgement for Linking Two Existing Scales

This article describes an efficient way of using comparative judgement to calibrate scores from different educational assessments against one another (a task often referred to as test linking or equating). The context is distinct from other applications of comparative judgement as there is no need to create a new achievement scale using a Bradley-Terry model (or similar). The proposed method takes advantage of this fact to include evidence from the largest possible number of examples of students’ performances on the separate assessments whilst keeping the amount of time required from expert judges as low as possible. The paper describes the method and shows, via simulation, how it achieves greater accuracy than alternative approaches to the use of comparative judgement for test equating or linking.
Intelligent Segmentation and Recognition Method of Breast Cancer Based on Digital Image Processing Technology

With the development of digital image technology, judging diseases by medical image plays an important role in medical diagnosis. Mammography is the most effective imaging examination method for breast cancer at present. Intelligent segmentation and identification of breast cancer images and judging their size and classification by digital image processing technology can promote the development of clinical medicine. This paper introduces the preprocessing technology of breast cancer pathological image and medical image recognition technology of breast cancer. In order to improve the segmentation accuracy of image processing and optimize, the segmentation recognition ability in digital mammography was improved. Based on the technical basis of pathological image analysis of breast cancer, the architecture of intelligent segmentation and recognition system for breast cancer was constructed, and each functional module of intelligent system was introduced in detail. Based on digital image processing technology, filtering technology is used to reduce dryness and improve the clarity of the image. Public datasets INBreast and DDSM-BCRP were used to verify system’s performance, and it was tested on the breast cancer image test set. The experiment shows that the comprehensive performance of the intelligent segmentation and recognition system can realize the segmentation and recognition of breast cancer and has higher accuracy and interpretability, which is helpful to improve the diagnosis of doctors.