Machine Learning Algorithms for Big Data Applications With Policy Implementation

This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed us to provide snapshots of different research topics and variation across covariates but also let us track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation.

Automatic BIRCH thresholding with features transformation for hierarchical breast cancer clustering

<p>Breast cancer is one of the most common diseases diagnosed in women over the world. The balanced iterative reducing and clustering using hierarchies (BIRCH) has been widely used in many applications. However, clustering the patient records and selecting an optimal threshold for the hierarchical clusters still a challenging task. In addition, the existing BIRCH is sensitive to the order of data records and influenced by many numerical and functional parameters. Therefore, this paper proposes a unique BIRCH-based algorithm for breast cancer clustering. We aim at transforming the medical records using the breast screening features into sub-clusters to group the subject cases into malignant or benign clusters. The basic BIRCH clustering is firstly fed by a set of normalized features then we automate the threshold initialization to enhance the tree-based sub-clustering procedure. Additionally, we present a thorough analysis on the performance impact of tuning BIRCH with various relevant linkage functions and similarity measures. Two datasets of the standard breast cancer wisconsin (BCW) benchmarking collection are used to evaluate our algorithm. The experimental results show a clustering accuracy of 97.7% in 0.0004 seconds only, thereby confirming the efficiency of the proposed method in clustering the patient records and making timely decisions.</p>

Educational Robotics and Attention to Diversity: A Case Study

<p style="text-align: justify;">In this study we focus our research on the case analysis of an eleven-year-old boy and his close relationship with technology, specifically robotics. The methodology of the study is experimental in nature, with the aim of improving the subject's attention span through robotics, thereby favouring his educational process and, consequently, his overall development. To this end, the attitudes, and aptitudes that this technological tool has provided the subject with are evaluated over a period of four years. Three data collection instruments were selected: questionnaire, interview, and observation. Among the conclusions we highlight, on the one hand, that the older the age and the greater the interest in robotics, the greater the individual's attention span and greater psychomotor coordination, increasing the improvement in the educational process and in their daily life. On the other hand, robotics is an effective way of orienting knowledge towards the personal and educational sphere and can provide advantages in integral development. </p>

Multi-Objective Optimization Information Fusion and Its Applications for Logistics Centers Maximum Coverage

From past the development direction of logistics centers covering problem, the main solution is almost always relying on modern computer and gradually developed intelligent algorithm, at the same time, the previous understanding of dynamic covering location model is not "dynamic", in order to improve the unreasonable distribution of logistics centers deployment time, improve the service coverage, coverage as the optimization goal to logistics centers, logistics centers as well as each one can be free to move according to certain rules of "dot", according to the conditions set by the site moved to a more reasonable. The innovation of all algorithms in this paper lies in that the logistics centers themselves are regarded as the subject of free "activities", and they are allowed to move freely according to these rules by setting certain moving rules. Simulation results show that the algorithm has good coverage effect and can meet the requirements of logistics centers for coverage effect.

A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. A specific form of fairness is supplier exposure fairness, where the objective is to ensure equitable coverage of items across all suppliers in recommendations provided to users. This is especially important in multistakeholder recommendation scenarios where it may be important to optimize utilities not just for the end user but also for other stakeholders such as item sellers or producers who desire a fair representation of their items. This type of supplier fairness is sometimes accomplished by attempting to increase aggregate diversity to mitigate popularity bias and to improve the coverage of long-tail items in recommendations. In this article, we introduce FairMatch, a general graph-based algorithm that works as a post-processing approach after recommendation generation to improve exposure fairness for items and suppliers. The algorithm iteratively adds high-quality items that have low visibility or items from suppliers with low exposure to the users’ final recommendation lists. A comprehensive set of experiments on two datasets and comparison with state-of-the-art baselines show that FairMatch, although it significantly improves exposure fairness and aggregate diversity, maintains an acceptable level of relevance of the recommendations.

Stochastic modelling of transition dynamic of mixed mood episodes in bipolar disorder

In the present state of health and wellness, mental illness is always deemed less importance compared to other forms of physical illness. In reality, mental illness causes serious multi-dimensional adverse effect to the subject with respect to personal life, social life, as well as financial stability. In the area of mental illness, bipolar disorder is one of the most prominent type which can be triggered by any external stimulation to the subject suffering from this illness. There diagnosis as well as treatment process of bipolar disorder is very much different from other form of illness where the first step of impediment is the correct diagnosis itself. According to the standard body, there are classification of discrete forms of bipolar disorder viz. type-I, type-II, and cyclothymic. Which is characterized by specific mood associated with depression and mania. However, there is no study associated with mixed-mood episode detection which is characterized by combination of various symptoms of bipolar disorder in random, unpredictable, and uncertain manner. Hence, the model contributes to obtain granular information with dynamics of mood transition. The simulated outcome of the proposed system in MATLAB shows that resulting model is capable enough for detection of mixed mood episode precisely

CRISTAL-ITE: a single-crystal X-ray diffractometer scale model for scientific dissemination

This article presents the design and manufacture of an automated scale model of a four-circle single-crystal X-ray diffractometer that can be used for scientific dissemination. The purpose of this device is to reach out to the wider public and students to introduce them in an entertaining way to one of the laboratory apparatuses to which they do not usually have access, to talk to them about crystallography in the broadest sense, to develop concepts in various fields of science and technology, and to initiate interest and discussions. The main technical aspects of the project are described, with the expectation that such an approach could be useful to anyone involved in scientific dissemination and could be developed for other laboratory equipment and other disciplines. This kind of device can also be the subject of scientific and technological projects in close collaboration with educational institutions.

The Impact of Dormant Defects on Defect Prediction: A Study of 19 Apache Projects

Defect prediction models can be beneficial to prioritize testing, analysis, or code review activities, and has been the subject of a substantial effort in academia, and some applications in industrial contexts. A necessary precondition when creating a defect prediction model is the availability of defect data from the history of projects. If this data is noisy, the resulting defect prediction model could result to be unreliable. One of the causes of noise for defect datasets is the presence of “dormant defects,” i.e., of defects discovered several releases after their introduction. This can cause a class to be labeled as defect-free while it is not, and is, therefore “snoring.” In this article, we investigate the impact of snoring on classifiers' accuracy and the effectiveness of a possible countermeasure, i.e., dropping too recent data from a training set. We analyze the accuracy of 15 machine learning defect prediction classifiers, on data from more than 4,000 defects and 600 releases of 19 open source projects from the Apache ecosystem. Our results show that on average across projects (i) the presence of dormant defects decreases the recall of defect prediction classifiers, and (ii) removing from the training set the classes that in the last release are labeled as not defective significantly improves the accuracy of the classifiers. In summary, this article provides insights on how to create defects datasets by mitigating the negative effect of dormant defects on defect prediction.

Introduction

The introduction outlines the subject of the research. One of the most relevant early medieval elite kinship groups of the Italian kingdom were the Hucpoldings, named after that Hucpold who had held the office of count palatine under Louis II. Key features of the research are the long chronological range and the wide geographical area investigated. The chapter then retraces the main historiographical steps taken in investigations of early medieval kinship groups from the second half of the twentieth century until the latest developments. A specific section is dedicated to the presentation and analysis of the documentary and narrative sources used in this research.