Reviewerly/exHarmony huggingface dataset:
Software Development Effort Duration and Cost Estimation using Linear Regression and K-Nearest Neighbors Machine Learning Algorithms

Effort estimation is a crucial step that leads to Duration estimation and cost estimation in software development. Estimations done in the initial stage of projects are based on requirements that may lead to success or failure of the project. Accurate estimations lead to success and inaccurate estimates lead to failure. There is no one particular method which cloud do accurate estimations. In this work, we propose Machine learning techniques linear regression and K-nearest Neighbors to predict Software Effort estimation using COCOMO81, COCOMONasa, and COCOMONasa2 datasets. The results obtained from these two methods have been compared. The 80% data in data sets used for training and remaining used as the test set. The correlation coefficient, Mean squared error (MSE) and Mean magnitude relative error (MMRE) are used as performance metrics. The experimental results show that these models forecast the software effort accurately.
A Robust Jamming Signal Classification and Detection Approach Based on Multi-Layer Perceptron Neural Network

The Quality of Service (QoS) and security of a wireless communication system can be improved using a robust Radio Frequency Interference (RFI) detection method, which could in turn lead to an effective mitigation process. Due to the vast variety of RFI signals in existence, it is crucial to deploy a spectrum signal detection technique dealing with all kinds of potential interference (non-intentional or intentional, known as jamming). To solve this problem, a robust multi-class Multi-Layer Perceptron (MLP) neural network technique has been developed in this paper to recognize jamming signals in a real-time digital video broadcasting scenario based on DVB-S2 standards. It is assumed that the communication signal can be combined with one of the three major kinds of interference, namely, Continuous Wave Interference (CWI), Multiple CWI (MCWI) and Chirp Interference (CI). The proposed algorithm can either classify the interference under its related group or recognize the Signal of Interest (SoI). Besides analyzing different learning approaches (such as online learning, full-batch, and mini-batch), the Principal Component Analysis (PCA) technique is deployed to select more informative features to enhance the classifier's performance.More importantly, a new methodology is presented to generate a real-time jamming signal dataset that is entirely different from what is provided in other similar studies.Furthermore, the robustness of the trained classifier is assessed to detect unknown signals at different Signal to Noise Ratios (SNR).In addition, the performance of the proposed design is compared with the well-known Support Vector Machine (SVM) Machine Learning (ML) technique in terms of both jamming signal classification and detection.
Metoo Movement Analysis through the Lens of Social Media

Sentiment analysis is an errand which is used to analyse people’s opinions which has been derived out of textual data seems productive for palpating various NLP applications. The grievances associated with this task is that, there prevails variety of sentiments within these documents, accompanied with diverse expressions. Therefore, it seems hard to whip out all sentiments employing a dictionary which is commonly used. This work attempts at constructing the domain sentiment dictionary, by employing the external textual data. Besides, various classification models could be utilised to classify the documents congruent to their opinion. We have also implemented topic modelling, emoticon analysis and optimized gender classification in our proposed system. Many sectors have been identified where women are being abused. Clusters are formed for these sectors and the most affected sector is also identified.
A Smart Model for Web Phishing Detection Based on New Proposed Feature Selection Technique

Web-phishing attacks are one of the most serious cybercrime. It enables hackers to access the devices of many users and spy on their personal data such as passwords and credit card details. Hackers use a lot of tricks through the internet, which make users to share data, download files or open links that attack a computer. This research proposes meta-heuristic based approach to protect the internet users from the web-phishing. It consists of three phases, the first phase uses a new proposed method for evaluating and ranking the features of URL, HTML and JavaScript code, text, images and domain name of the web page. The second phase extracts the effective subset of the ranked features that achieves the highest classification accuracy of the web-phishing. The third phase constructs the Random forest classifier training by data features of the extracted subset. The new proposed method of the feature selection achieved the highest classification accuracy compared to the correlation feature selection, information gain, principle component analysis, and Relief feature selection algorithms. The proposed methodology of the web-phishing detection was also evaluated, it obtained the highest classification accuracy at the least possible time compared to the adaptive Neuro-fuzzy inference system.
Design of Pseudo Random Number Generator using Linear Feedback Shift Register

Nowadays security has become a great concern in the field of computer science and information technology. In order to protect data from unintended users and to achieve a desirable level of security, several cryptographic algorithms based on various technology have been proposed. Linear Feedback Shift Register (LFSR) may play an important role in the design of such cryptographic algorithms. LFSR based cryptographic algorithms are often lightweight in nature and are more suitable for resource constraining devices. In this paper we present a detailed analysis of LFSR and design of LFSR to implement cryptographic algorithms.
Sentiment Analysis Using Lstm

Extracting the sentiment of the text using machine learning techniques like LSTM is our area of concern. Classifying the movie reviews using LSTM is our problem statement. The reviews dataset is taken from the IMDB movie review dataset. Here we will classify a review based on the memory in the neural network of a LSTM cell state. Movie reviews often contain sensible content which describe the movie. We can manually decide whether a movie is good or bad by going through these reviews. Using machine learning approach we are classifying the movie reviews such that we can say that a movie is good or bad. LSTM is effective than many other techniques like RNN and CNN.
Negation scope detection with recurrent neural networks models in review texts

Identifying negation scopes in a text is an important subtask of information extraction that can benefit other natural language processing tasks, like relation extraction, question answering and sentiment analysis, and serves the task of social media text understanding. The task of negation scope detection can be regarded as a token-level sequence labelling problem. In this paper, we propose different models based on recurrent neural networks (RNNs) and word embedding that can be successfully applied to such tasks without any task-specific feature engineering effort. Our experimental results show that RNNs, without using any hand-crafted features, outperform feature-rich CRF-based model.
RF Fingerprint Extraction Method Based on CEEMDAN and Multidomain Joint Entropy

Specific emitter identification (SEI) can distinguish communication radio emitters with the fingerprint features carried by the received signal, and this technology has been widely used in military and civilian fields. However, in the real electromagnetic environment, the number of communication radio emitters is large and the signal-to-noise ratio (SNR) is low, which leads to poor nonlinear fingerprint analysis of SEI in a single domain. Therefore, combining the exploration of multiple domains of electromagnetic spatial information resources, this paper proposed a radio frequency (RF) fingerprint extraction method based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multidomain joint entropy. The proposed method is an attempt and exploration further extraction of nonlinear fingerprint features in multiple domains. Firstly, considering the nonstationarity of the communication signal, this article adopts the CEEMDAN method to decompose the signal to multiple intrinsic mode functions (IMF). Then, the decomposed signal is represented in multiple spaces by a multidimensional phase space reconstruction technique. Nonlinear analysis of the original signal is performed in multiple spaces: multidimensional differential approximate entropy space, singular spectral entropy space, and power spectral entropy space. Finally, the support vector machine (SVM) is adopted in the classification stage. To demonstrate the robustness of the method, the method is verified on the universal software radio peripheral (USRP) dataset and the Northeastern University public dataset. In terms of the identification accuracy, the proposed method performs with 98.5% accuracy on the 5-class USRP dataset. It also performs with 94.7% accuracy on the 16-class public dataset. The experimental results show that the proposed method has a stable identification performance and has a more than 85% recognition rate in the SNR above 5dB.
Business Transformation Project’s Holistic Agile Management (BTPHAM)

This article proposes a Business Transformation Project (BTP), Holistic Agile Management (HAM), to support intensive agile transformations. A BTP assisted by HAM (BTPHAM) uses critical success factors and areas, natural programming language environment and a dynamic decision-making system to improve the enterprise’s Time To Market (TTM). BTPHAM supports all phases of a transformation project, and its concept is mainly based on existing standards, methodologies, and conventional practices. Complex markets and high levels of competition forces enterprises to integrate agile product and services management, in all its value chains. A HAM approach forces the used transformation framework and the related set of existing frameworks, to synchronize all types of transformation activities, like the deployment of software components. Such a synchronization process may generate many problems, which are difficult to trace, and which can damage the cyclic development process. This framework’s originality is that it can be used in any stage of the transformation project and for any type of problem, and to synchronize BTPHAM’s coordination. The main limitations are the enterprise’s capacity to restructure and unbundle its legacy environments; and to integrate the BTPHAM in all its environments and implementation processes; which need the optimal BTP manager.
Discrimination of Mineralization Types of Skarn Deposits by Magnetite Chemistry

There are different mineralization types for skarn deposits with various origins and ore-forming conditions. Magnetite is one of the main ore minerals in skarn deposits, but whether chemical compositions of magnetite can be used to discriminate different mineralization types remains unknown. This paper collects the published magnetite electron probe microanalysis (EPMA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) data of skarn deposits and investigates the relationship between magnetite geochemistry and mineralization types of skarn deposits using the partial least squares-discriminant analysis (PLS-DA). For EPMA data, magnetite from Fe-Zn skarn deposits can be roughly separated from that of Cu-Fe-Pb-Zn, Fe, Fe-Co-Bi-Ag, Fe-Cu, and Fe-Zn-Pb skarn deposits due to the relative enrichment of Al and Mn for the former. For LA-ICP-MS data, magnetite from Fe-Sn, Fe-Zn, and W-Mo-Pb-Zn-Fe-Cu skarn deposits can be roughly separated from that of other skarn deposits due to positive correlation with Mn, Zn, and Sn and the negative correlation with V for the former. The relative depletion of V for these mineralization types likely reflects higher oxygen fugacity than the other types of skarn deposits. Magnetite from Fe-Au skarn deposits is separated due to the relatively high Cr and Ga contents, whereas magnetite from Fe-Cu skarn deposits can be discriminated because of the relative enrichment of Mg and Co. The discrimination between different types of skarn deposits in the plot of Mg + Mn vs. (Si + Al)/(Mg + Mn) indicates that the chemical composition of magnetite is significantly affected by the fluid–rock interaction, where magnetite from Fe-Au skarn deposit shows the lowest fluid–rock ratios. The PLS-DA discrimination based on LA-ICP-MS data is better than that of EPMA data, and the main discriminant elements for the different mineralization types are Mg, Al, Ti, V, Mn, Co, Zn, Ga, and Sn. Based on the discriminant elements, we propose a plot of Mg+Mn vs. Ga+Sn to discriminate different mineralization types of skarn deposits.
Complex-valued Parallel Convolutional Recurrent Neural Networks for Automatic Modulation Classification

Following the great success of deep learning in signal processing, Many models based on real-valued convolutional neural networks (CNNs) and recurrent neural networks (RNNs) have been proposed for automatic modulation classification (AMC). However, the modulation signal is not only temporally dependent but also complex-valued data. The real-valued deep learning models treat the real and imaginary parts of the complex-valued modulation signal as two independent real-valued inputs, which destroy the structure of the raw signal data and make the model more uninterpretable. Thus, this paper proposes a novel complex-valued parallel convolutional recurrent neural network (CPCRNN) specifically for AMC. CPCRNN combines parallel complex-valued CNN and RNN with redesigned complex-valued activation function and complex-valued max pooling. The model directly feeds the complex-valued raw signal to the complex-valued CNN to obtain the complex-valued feature maps, which are then transformed into amplitude and phase and fed to the RNN. Our model can first extract the complex-valued features of the modulation signal with complex-valued CNNs, and then extract the temporal features of the modulation signal with RNNs. CPCRNN achieved an overall accuracy of 62.29% and 69.93% on the benchmark datasets RadioML2016.10A and RadioML2018.01-simple, respectively, outperforming all the state-of-the-art algorithms.
Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability

Abstract Background Prognostic models that are accurate could help aid medical decision making. Large observational databases often contain temporal medical data for large and diverse populations of patients. It may be possible to learn prognostic models using the large observational data. Often the performance of a prognostic model undesirably worsens when transported to a different database (or into a clinical setting). In this study we investigate different ensemble approaches that combine prognostic models independently developed using different databases (a simple federated learning approach) to determine whether ensembles that combine models developed across databases can improve model transportability (perform better in new data than single database models)? Methods For a given prediction question we independently trained five single database models each using a different observational healthcare database. We then developed and investigated numerous ensemble models (fusion, stacking and mixture of experts) that combined the different database models. Performance of each model was investigated via discrimination and calibration using a leave one dataset out technique, i.e., hold out one database to use for validation and use the remaining four datasets for model development. The internal validation of a model developed using the hold out database was calculated and presented as the ‘internal benchmark’ for comparison. Results In this study the fusion ensembles generally outperformed the single database models when transported to a previously unseen database and the performances were more consistent across unseen databases. Stacking ensembles performed poorly in terms of discrimination when the labels in the unseen database were limited. Calibration was consistently poor when both ensembles and single database models were applied to previously unseen databases. Conclusion A simple federated learning approach that implements ensemble techniques to combine models independently developed across different databases for the same prediction question may improve the discriminative performance in new data (new database or clinical setting) but will need to be recalibrated using the new data. This could help medical decision making by improving prognostic model performance.
An Assessment of Eclipse Bugs' Priority and Severity Prediction Using Machine Learning

The reliability and quality of software programs remains to be an important and challenging aspect of software design. Software developers and system operators spend huge time on assessing and overcoming expected and unexpected errors that might affect the users’ experience negatively. One of the major concerns in developing software problems is the bug reports, which contains the severity and priority of these defects. For a long time, this task was performed manually with huge effort and time consumptions by system operators. Therefore, in this paper, we present a novel automatic assessment tool using Machine Learning algorithms, for assessing bugs’ reports based on several features such as hardware, product, assignee, OS, component, target milestone, votes, and versions. The aim is to build a tool that automatically classifies software bugs according to the severity and priority of the bugs and makes predictions based on the most representative features and bug report text. To perform this task, we used the Multi-Nominal Naive Bayes, Random Forests Classifier, Bagging, Ada Boosting, SVC, KNN, and Linear SVM Classifiers and Natural Language Processing techniques to analyze the Eclipse dataset. The approach shows promising results for software bugs’ detection and prediction.
Field hyperspectral data and OLI8 multispectral imagery for heavy metal content prediction and mapping around an abandoned Pb–Zn mining site in northern Tunisia

Mining and smelting releases toxic contaminants such as zinc (Zn), lead (Pb) or cadmium (Cd) into the soil thereby poisoning it and rendering it unproductive. Remotely alternatives have been widely employed in the attempt of estimating heavy metal content within soils. The present study provides a methodological approach based on VNIR-SWIR field hyperspectral data and multispectral Landsat OLI 8 imageries for the prediction and mapping of Pb, Zn and Cd heavy metal contents around the abandoned Jebel Ressas mine site in Northern Tunisia. Thus, eighty-seven soil and tailing samples were collected from the study site and VNIR-SWIR field reflectances were measured on the same collection points, as well. All samples were analysed by atomic absorption for the estimation of heavy metal concentrations. The partial least squares regression PLSR was conducted considering the measured heavy metal concentrations and using multi-scale data: VNIR-SWIR field hyperspectral data and multispectral Landsat OLI 8 imagery. Standard normal variable (SNV) and multiple scatter correction (MSC) preprocessing methods were applied for further mapping improvement. Thus, this work aims to automate the estimation of the heavy metal contents in contaminated soils, by carrying out: a modeling approach based on the PLSR using VNIR-SWIR field hyperspectral data, ii) the mapping of Pb and Zn contents thanks to the exploitation of Landsat OLI8 multispectral imagery and iii) the application of both MSC and SNV preprocessing methods to optimize the performance of the developed models, when using such spectrally and spatially degraded data.
A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images

Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open-source machine learning method for the segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of the data. Our results show that the resulting TIL quantification correlates to the patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small cell lung cancer (current standard CD8 cells in DAB-stained TMAs HR 0.34, 95% CI 0.17-0.68 vs. TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30, 95% CI 0.15-0.60 and HoVer-Net MoNuSAC Aug model HR 0.27, 95% CI 0.14-0.53). Our approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, further validation is warranted before implementation in a clinical setting.
Constrained unsupervised anomaly segmentation

Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regions derived from errors on the reconstructed images. To address the limitations of residual-based anomaly localization, very recent literature has focused on attention maps, by integrating supervision on them in the form of homogenization constraints. In this work, we propose a novel formulation that addresses the problem in a more principled manner, leveraging well-known knowledge in constrained optimization. In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility. In addition, to address the limitations of penalty-based functions we employ an extension of the popular log-barrier methods to handle the constraint. Last, we propose an alternative regularization term that maximizes the Shannon entropy of the attention maps, reducing the amount of hyperparameters of the proposed model. Comprehensive experiments on two publicly available datasets on brain lesion segmentation demonstrate that the proposed approach substantially outperforms relevant literature, establishing new state-of-the-art results for unsupervised lesion segmentation.