Reviewerly/exHarmony huggingface dataset:
A Multi-agent Reinforcement Learning Risk Management Model for Distributed Agile Software Projects

Nowadays, due to the benefits of the agile method, such as changing rapidly and fast delivery of working software, most software projects are naturally distributed agile projects (DAD). DAD team members work from various remote sites. This leads to the emergence of many significant challenges in risk management. Hence, it requires risk safety techniques to be implemented to mitigate the occurrence of risks. There is no standardized process for teams to deal with DAD risks. In this paper, a multi-agent reinforcement learning risk management model for distributed agile software projects is proposed. The proposed model is implemented to apply a dynamic policy. The model is applied as an experiment to definite numbers for a set of risk factors such as communication and coordination risk, project management risk, and SDLC risk. The proposed model is developed using multiple individual learning using the Q- learning algorithm. A DAD project with two projects is used to evaluate the proposed model. The proposed model was assessed and analyzed for its effectiveness. The results of the evaluation are given.
An Effective Nature Inspired Approach for the Estimation of Software Development Cost

Accurate estimation of software cost and time is one of the important aspects of software project development. Accurate cost and effort predictions for software projects have a significant impact, meta-heuristic algorithms assist the software industry in predicting trustworthy and reliable values for software project planning and maintenance. Using these algorithms increases the software project success rate, in parallel with using the project development and management approaches of the current practices. Traditional software estimation techniques have deficiencies that are resolved by using meta-heuristic algorithms. Due to unclear model building and inaccurate results, few or none of these approaches are used. The motivation behind this research is to limit the gap between modern research results and executions inside organisations by proposing practical and effective nature-inspired algorithms for deployment and support approaches through the usage of research findings and industry-current practices. This research was accomplished by using the NASA 93 dataset, data selection, data distribution, and take-averaging on 3 meta-heuristic algorithms such as the Strawberry Algorithm (SBA), Gray Wolf Algorithm (GWO), and Harmony Search Algorithm (HSA). The inclusion criteria of these algorithms are high maturity, state-of-the-art, and representative. The purpose of algorithmic optimization is to minimise the Mean Magnitude of Relative Error (MMRE) and efficiently manage the activities of software development within estimated time and cost. These algorithms are evaluated based on their Magnitude of Relative Error (MRE) / Mean Magnitude of Relative Error (MMRE). GWO performs best in terms of MMRE reduction.
Algebraic Constructions of Complete m-Arcs

Let m be a positive integer, q be a prime power, and PG(2,q) be the projective plane over the finite field $${\mathbb{F}_q}$$ . Finding complete m-arcs in PG(2,q) of size less than q is a classical problem in finite geometry. In this paper we give a complete answer to this problem when q is relatively large compared with m, explicitly constructing the smallest m-arcs in the literature so far for any m ≥ 8. For any fixed m, our arcs $${{\cal A}_{q,m}}$$ satisfy $$\left| {{{\cal A}_{q,m}}} \right| - q \to - \infty $$ as q grows. To produce such m-arcs, we develop a Galois theoretical machinery that allows the transfer of geometric information of points external to the arc, to arithmetic one, which in turn allows to prove the m-completeness of the arc.
Discrete-Time Mean Field Control with Environment States

Multi-agent reinforcement learning methods have shown remarkable potential in solving complex multi-agent problems but mostly lack theoretical guarantees. Recently, mean field control and mean field games have been established as a tractable solution for large-scale multi-agent problems with many agents. In this work, driven by a motivating scheduling problem, we consider a discrete-time mean field control model with common environment states. We rigorously establish approximate optimality as the number of agents grows in the finite agent case and find that a dynamic programming principle holds, resulting in the existence of an optimal stationary policy. As exact solutions are difficult in general due to the resulting continuous action space of the limiting mean field Markov decision process, we apply established deep reinforcement learning methods to solve the associated mean field control problem. The performance of the learned mean field control policy is compared to typical multi-agent reinforcement learning approaches and is found to converge to the mean field performance for sufficiently many agents, verifying the obtained theoretical results and reaching competitive solutions.
A raster-based typification method for multiscale visualization of building features considering distribution patterns

In map multiscale visualization, typification is the process of replacing original objects, such as buildings, using a smaller number of objects while maintaining initial geometrical and distribution characteristics. During the past few decades, many vector-based methods for building typification have been developed, whereas raster-based methods have received less attention. In this paper, a new method for the typification of buildings with different distribution patterns called superpixel building typification (SUBT) is developed based on raster data. Using this method, buildings with different distribution patterns, such as linear, grid and irregular patterns, are first grouped by image connected component detection and superpixel analysis. Then, the new positions for building typification are determined by superpixel resegmentation. Finally, a new representation of the buildings is determined through analysis of the orientation and shape of the buildings in each superpixel. To test the proposed SUBT method, buildings from both cities and countrysides in China are applied to perform typification. The experimental results show that the proposed SUBT method can realize typification for buildings with linear, grid and irregular distributions while effectively maintaining the original distribution characteristics of the buildings.
Exploiting GPUs for fast intersection of large sets

The main focus of this work is on large set intersection, which is a pivotal operation in information retrieval, graph analytics and database systems. We aim to experimentally detect under which conditions, using a single graphics processing unit (GPU) is beneficial over CPU techniques and which exact techniques are capable of yielding improvements. We cover and adapt techniques initially proposed for graph analytics and matrix multiplication, while we investigate new hybrids for completeness. We also explain how we can address set containment joins using the same techniques. The comprehensive evaluation highlights the main characteristics of the techniques examined when both a single pair of two large sets are processed and all pairs in a dataset are examined, while it provides strong evidence that state-of-the-art set containment stands to significantly benefit from advances in GPU-enabled set intersection. Our results reveal that there is no dominant solution but depending on the exact problem and the dataset characteristics, different techniques are the most efficient ones. • Develop hybrid GPU set intersection techniques for set sizes on the order of millions. • GPU’s fast matrix multiplication greatly improves set intersection for dense datasets. • GPU-based set intersection techniques are superior over CPU parallel alternatives. • A co-processing CPU–GPU scheme further improves performance for set containment join.
Automatic Prostate Cancer Grading Using Deep Architectures

Prostate cancer is the second most aggressive type of cancer among men aged over 45, and it has a major effect on people's lives. Early diagnosis and grading of prostate cancer from tissue images is necessary. Large scale inter observer reproducibility exists in grading the prostate biopsies. This leads us to move towards a computer based model that can accurately detect and grade the cancerous prostate from non-cancerous one. The paper is focused on deep learning based models to automatically grade the prostate cancer from tissue microarray images. Deep learning models directly learn the features via convolutional layers. Two datasets have been used for implementation of our proposed model, Harvard dataset and Gleason Challenge 2019. Our proposed UNET based architecture is used for training as well as validation and testing. We used four different deep learning models, VGG19, ResNet50, Mobilenetv2 and ResNext50 for our UNET based encoder. With our proposed framework, we have achieved 0.728 and 0.732 average Cohen’s kappa with F1 on both datasets respectively. The results show that our proposed UNET based deep learning model shows better performance as compared to other state of the art models.
Sentiment Analysis of Chinese Text Based on CNN-BiLSTM Serial Hybrid Model

Considering the forward and backward dependencies of words in Chinese sentences, this paper combines convolutional neural networks (CNN) and bi-directional long short-term memory (BiLSTM) according to a certain framework to form CNN- BiLSTM model, and uses this model to complete the task of emotional classification of Chinese texts. The model uses CNN to extract sentence features and then uses BiLSTM to capture two-way semantics to realize text sentiment classification. Through verification on Sina Weibo comments data set, CNN-BiLSTM can obtain better accuracy, recall and F1-score compared to CNN, RNN, LSTM and BiLSTM. It shows that CNN-BiLSTM can achieve better performance in the field of Chinese sentiment classification.
Mixture regression for longitudinal data based on joint mean–covariance model

In the process of modeling longitudinal data, we focus on the case that the studied population is comprised of different groups of individuals and individuals within the same group share the similar kind of mean progression trajectories, where finite mixture models (FMM) are often used to address this kind of unobserved heterogeneity in terms of mean. Existing methods, such as parametric and semiparametric mixture regression, usually model the mean in each subpopulation with assumption that observations sharing a common trajectory are independent or their covariance structure is pre-specified, but less research considers modeling of covariance structures while accounting for heterogeneity. In this paper, we introduce a joint model which models the mean and covariance structures simultaneously in a finite normal mixture regression, demonstrating how important the within-subject correlation is in clustering longitudinal data. Model parameters are estimated with an iteratively re-weighted least squares EM (IRLS-EM) algorithm. Our estimators are shown to be consistent and asymptotically normal. We can identify different mean trajectories and covariance structures in all clusters. Simulations show that the proposed method performs well and gives more accurate clustering results by introducing covariance modeling. Real data analysis is also used to illustrate the usefulness of the proposed method, and it presents good performance in clustering COVID-19 deaths for European countries in terms of progression trajectory.
Dense channel splitting network for MR image super-resolution

High spatial resolution is desirable in magnetic resonance imaging (MRI) as it can provide detailed anatomical information, facilitating radiologists with accurate quantitative analysis. Super-resolution (SR) algorithms are effective approaches to enhance MR images' spatial resolution. In the past few years, convolutional neural network (CNN)-based SR methods have significantly improved and outperformed conventional ones. However, existing CNN-based SR methods usually do not explicitly consider the frequency property of images, leading to the limited representation of high-frequency components reflecting image details. To alleviate this problem, a dense channel splitting network (DCSN) algorithm is proposed to process the frequency bands for better feature detection. Specifically, a channel splitting module, a cascaded multi-branch dilation module, and a dense-in and recursive-out mechanism are designed to separate frequency bands of MR images and forward the high-frequency information to deeper layers for reconstruction. Several experiments are performed on real T2 brain and PD (proton density) knee images. The results indicate that the proposed network is superior to conventional CNN-based SR methods.
Application of a FL Time Series Building Model in Mobile Network Interaction Anomaly Detection in the Internet of Things Environment

With the continuous development of the social economy, mobile network is becoming more and more popular. However, it should be noted that it is vulnerable to different security risks, so it is extremely important to detect abnormal behaviors in mobile network interaction. This paper mainly introduces how to detect the characteristic data of mobile Internet interaction behavior based on IOT FL time series component model, set the corresponding threshold to screen the abnormal data, and then use K-means++ clustering algorithm to obtain the abnormal set of multiple interactive data, and conduct intersection operation on all abnormal sets, so as to obtain the final abnormal detection object set. The simulation results show that the FL time series component model of the Internet of Things is effective and can support abnormal detection of mobile network interaction behavior.
Multiple importance unscented Kalman filtering with soft spatiotemporal constraint for multi‐passive‐sensor target tracking

Abstract Multi‐passive‐sensor systems are a common means for the target tracking and their bearings processing is a prerequisite for stable control and nonlinear filtering. This study proposes a mathematical methodology that is based on the incorporating deterministic unscented transition rules into stochastic sequential importance sampling frame and makes use of soft spatiotemporal constraint comprise multiview epipolar geometry constraint and numerical regularization to solve the correspondence problem. A prototype measurement‐driven target tracking frame was developed that can work in real time and achieve filtering improvements of 41%–46% and 43%–48% in terms of root‐mean‐square error and root time‐averaged mean square error compared with the state‐of‐the‐art multiple model Rao–Blackwell particle filtering method, as proven by the simulation results.
Pattern recognition method from hydrochemical parameters to predict uranium concentrations in groundwater

Traditional methods for determining uranium in groundwater are spectroscopic methods that require time, money, and experienced chemists. A prediction method for predicting uranium concentration using a few hydrochemical parameters that are related to uranium was developed within this study. In this study, uranium, hydrochemical parameters, and trace elements of groundwater around Ulaanbaatar in Mongolia were measured. Inductively coupled plasma mass spectrometry was used to determine the uranium concentration in 135 samples. The relationship between uranium concentration and hydrochemical parameters was studied to determine whether the concentration in groundwater could be predicted using chemometric methods based on some hydrochemical parameters. Chemometric methods were performed using a Python programming language. A pattern recognition method for classifying groundwater samples by specific threshold uranium concentration uses a principal component analysis (PCA) and support vector machine (SVM). The average accuracy of the classification models was 88.21%. PCA is used to visualize the classification by uranium concentration and show which hydrochemical parameters are crucial to predicting uranium concentration. In this study, a regression model was developed to predict uranium concentration in groundwater using hydrochemical parameters selected by the PCA-SVM combination method. The regression method uses a combination of polynomial regression and multiple linear regression. This combined regression method has shown good results for predicting uranium concentrations based on selected hydrochemical parameters.
Morph Creation and Vulnerability of Face Recognition Systems to Morphing

Abstract Face recognition in controlled environments is nowadays considered rather reliable, and very good accuracy levels can be achieved by state-of-the-art systems in controlled scenarios. However, even under these desirable conditions, digital image alterations can severely affect the recognition performance. In particular, several studies show that automatic face recognition systems are very sensitive to the so-called face morphing attack, where face images of two individuals are mixed to produce a new face image containing facial features of both subjects. Face morphing represents nowadays a big security threat particularly in the context of electronic identity documentsbecause it can be successfully exploited for criminal intents, for instance to fool Automated Border Control (ABC) systems thus overcoming security controls at the borders. This chapter will describe the face morphing process, in an overview ranging from the traditional techniques based on geometry warping and texture blending to the most recent and innovative approaches based on deep neural networks. Moreover, the sensitivity of state-of-the-art face recognition algorithms to the face morphing attack will be assessed using morphed images of different quality generated using various morphing methods to identify possible factors influencing the probability of success of the attack.
Channel metrization

We present an algorithm that, given a channel, determines if there is a distance for it such that the maximum likelihood decoder coincides with the minimum distance decoder. We also show that any metric, up to a decoding equivalence, can be isometrically embedded into the hypercube with the Hamming metric, and thus, in terms of decoding, the Hamming metric is universal.
Video Satellite Imagery Super-Resolution via Model-Based Deep Neural Networks

Video satellite imagery has become a hot research topic in Earth observation due to its ability to capture dynamic information. However, its high temporal resolution comes at the expense of spatial resolution. In recent years, deep learning (DL) based super-resolution (SR) methods have played an essential role to improve the spatial resolution of video satellite images. Instead of fully considering the degradation process, most existing DL-based methods attempt to learn the relationship between low-resolution (LR) satellite video frames and their corresponding high-resolution (HR) ones. In this paper, we propose model-based deep neural networks for video satellite imagery SR (VSSR). The VSSR is composed of three main modules: degradation estimation module, intermediate image generation module, and multi-frame feature fusion module. First, the blur kernel and noise level of LR video frames are flexibly estimated by the degradation estimation module. Second, an intermediate image generation module is proposed to iteratively solve two optimal subproblems and the outputs of this module are intermediate SR frames. Third, a three-dimensional (3D) feature fusion subnetwork is leveraged to fuse the features from multiple video frames. Different from previous video satellite SR methods, the proposed VSSR is a multi-frame-based method that can merge the advantages of both learning-based and model-based methods. Experiments on real-world Jilin-1 and OVS-1 video satellite images have been conducted and the SR results demonstrate that the proposed VSSR achieves superior visual effects and quantitative performance compared with the state-of-the-art methods.
Evaluation of Transfer Learning based Deep Learning architectures for Waste Classification

Today, the development and modernization have led to the generation of waste, which has become a problem for all the living beings and the environment, whether it is medical waste, of which 25% is hazardous, or household waste, which contains harmful plastic. In this work, a deep learning solution has been provided towards the classification of a few classes of wastes such as plastic, paper, metal, glass, cardboard, etc. In this work, transfer learning has been used and applied. This helps deep learning models to accomplish the classification task in the most accurate way. The models such as EfficientNet, ResNet34, Densenet121, ResNeXt-50 32x4d, Wide ResNet50_2, Densenet169 are used here in this work. Extensive experimentation was done with the different optimizers. The training such as Adam optimizer, RMSprop optimizer, and Adadelta was performed and the experimental results demonstrate that the Adam optimizer produced the best results over the other competing methods. The proposed work has achieved a test accuracy of 98.02% using ResNeXt-50 32x4d and 95.8% using Wide ResNet50_2 architecture.
Predatory Conversation Detection Using Transfer Learning Approach

Predatory conversation detection on social media can proactively prevent the netizens, including youngsters and children, from getting exploited by sexual predators. Earlier studies have majorly employed machine learning approaches such as Support Vector Machine (SVM) for detecting such conversations. Since deep learning frameworks have shown significant improvements in various text classification tasks, therefore, in this paper, we propose a deep learning-based classifier for detecting predatory conversations. Furthermore, instead of designing the system from the beginning, transfer learning has been proposed where the potential of the pre-trained BERT (Bidirectional Encoder Representations from Transformers) model is utilized to solve the predator detection problem. BERT is mostly used to encode the textual information of a document into its context-aware mathematical representation. The inclusion of this pre-trained model solves two major problems, i.e. feature extraction and Out of Vocabulary (OOV) terms. The proposed system comprises two components: a pre-trained BERT model and a feed-forward neural network. To design the classification system with a pre-trained BERT model, two approaches (feature-based and fine-tuning) have been used. Based on these approaches two solutions are proposed, namely, BERT_frozen and BERT_tuned where the latter approach is seen performing better than the existing classifiers in terms of $$F_1$$ and $$F_{0.5}$$ -scores.
Knowledge Graphs

Since the introduction of the Google Knowledge Graph in the early 2010s, web search and advertising have both undergone profound shifts. A Knowledge Graph (KG) is a graph-based way of providing entities, relations, events, facts, and other aspects of knowledge to machines. By representing such objects formally, data science and Artificial Intelligence (AI) algorithms are able to “understand” sophisticated queries and comprehend complex user needs, including recommendations, allowing for new possibilities to be unlocked. Beyond the Google Knowledge Graph and generic web search, however, the application of KGs has been less prominent across the industry. In the commercial domain, product-related e-commerce sectors have been a major area of focus, as embodied in efforts such as the Amazon Product Graph, but the same cannot be said of service-related sectors such as tourism. Therefore, this is an opportunity waiting to be exploited. In this book chapter, some of the fundamentals of KGs will be explained, along with details on how a KG can be built and used within the tourism domain. Practical guidelines on implementation will also be discussed, drawing on existing technological tools, as well as a set of hints and tips.