Reviewerly/exHarmony huggingface dataset:
Neural Networks

Neural networks has its roots in the 1950s with the “perceptron.” It is essentially sets of regression equations linked end to end in a manner that allows for nonlinear relationships. Neural networks performs no better than more recent statistical learning tools and typically, worse. However, about 10 years ago, computer scientists recognized that a special data management front end could make neural networks extremely effective in classifying images, translating languages, and recognizing speech. Various elaborations and extensions of neural networks followed that some like to call “deep neural networks,” or more broadly, “deep learning.” These newer developments have generated both genuine excitement and some self-serving hype. In this chapter, we will begin with early neural networks and end with some of the impressive recent advances.
Population Based Search

This chapter introduces population based search methods and their R implementations, namely genetic and evolutionary algorithms, differential evolution, particle swarm optimization, ant colony optimization, estimation of distribution algorithm, genetic programming, and grammatical evolution. The chapter also presents examples of how to compare population based methods, how to handle constraints, and how to run population based methods in parallel.
Statistical survey

In order to begin to contemplate the challenges facing the management and examination of digital evidence, it is useful to understand the scale of the challenge. The scale is reflected in the numbers with which we are dealing. As we work our way through this volume, we will see that the field continues to change rapidly and unimaginably as new technologies become available. The growth in the user numbers of various technologies, the volume of data and the diversity of the technologies all present additional challenges to the digital evidence environment. The statistics listed in this section are just a sample of those that are available and point to the impact on the task of examining digital evidence, no matter in which context it is being applied.
Object-Oriented Programming

The programs so far have been primarily using a procedural style of programming. The procedural coding style is a good way to learn programming, but there are benefits to using object-oriented programming, which will be covered next. A useful thing about Python is that it supports many different coding styles, even allowing multiple styles in the same code. You have already been using some object-oriented code when making use of Python modules, including Pygame Zero.
Reading and Writing

This chapter focuses on two issues. Firstly, we consider the critical reading of research articles that use MLA, and secondly we explore the standards for writing up research that has used MLA. Critical reading is important both for people who do not regularly use MLA themselves and for those who are regular users. The irregular users need to be able to assess the methodology of studies using MLA, whilst regular users may find inspiration for new ways and strategies of data analysis and for ways to write up and present their own research, particularly the methods and results sections. So the reading and writing parts of this chapter are related. When a method of analysis is used that is relatively new to its field, there are no clear standards as to what should be included in the methods section or how the tables might be laid out.
Association Rule Mining I

This chapter looks at the problem of finding any rules of interest that can be derived from a given dataset, not just classification rules as before. This is known as Association Rule Mining or Generalised Rule Induction. A number of measures of rule interestingness are defined and criteria for choosing between measures are discussed. An algorithm for finding the best $N$ rules that can be generated from a dataset using the $J$-measure of the information content of a rule and a ‘beam search’ strategy is described.
Control-Flow Integrity Enforcement with Dynamic Code Optimization

Prior to the introduction of CFI in 2005, there have already been a lot of research on dynamic code optimization to improve performance of dynamic program interpreters. For example, Wiggins/Redstone [], Dynamo [], Mojo [], and DynamoRIO [].
Group-Oriented Cryptosystem for Personal Health Records Exchange and Sharing

Personal health records (PHRs) have been developed into a type of patient-centered health information exchange model in recent years. PHRs are concentrated in a data center established by a service provider through the Internet. From any location, at any time, with only a web browser and an Internet connection, users can access the PHR service provider to establish, control, and manage their personal health data. This PHR standard enables powerful saving, reading, and sharing of medical information. PHR management in the cloud must consider the security of the cloud computing environment. Apart from traditional service providers encrypting healthcare records, PHR cloud providers should provide a group-oriented cryptosystem for users. Accordingly, the proposed bilinear pairing-based group-oriented cryptosystem has the following advantages: (1) The cryptosystem can simultaneously realize four decryption strategies, enabling receivers to designate appropriate decryptors according to the content of plaintext. (2) All group members need only one private key, which can be used for decryption regardless of the decryption modes. Therefore, errors resulting from the misuse of keys can be avoided, and the difficulty of key management can be reduced. (3) The system is required to disclose only six parameters, thus decreasing spatial complexity. (4) Regardless of the encryption and decryption modes, receivers must perform encryption only one time, and the length of the ciphertext comprises only four parameters. Thus, the proposed cryptosystem computing (including environment setting and the processes of encryption and decryption) is highly efficient, with easy key management, low spatial complexity, and small amount of ciphertext being transmitted.
Cognitive Ergonomics in 2016

Cognitive ergonomics deals with decision making, skilled performance(s), and training. This chapter will show what began as a cognitive task analysis (CTA) for identifying critical skills needed for skilled performance, with decision making involved in every step. Over the years, it evolved into a paradigm that extended far beyond a CTA. With the advances in technology, especially communication technologies, some of this paradigm has re-emerged in what are termed estimate-talk-estimate (ETE) efforts, which can be web-based. ETE's are aimed at forecasting, decision making, even policymaking. This chapter will show how to integrate the elements of paradigm cited above with elements of Pareto analysis ETE in order to use today's technology to achieve extremely accurate results across many venues. The model that results from this integration will be used on three current problem areas to show how decisions can be made on standards, policy, and training needs. The problem areas encompass national education standards, aviation and operational testing (OT), and evaluation.
Developing Cybersecurity Resilience in the Provincial Government

The approach that the organization uses to manage its cyber-risk to its workforce, information, systems, and networks is paramount to ensure sustainability and continuity in the Fourth Industrial Revolution. Improving cyber-resiliency in the organization reduces the chance of future threats and attacks and builds better capability. Cyber resilience involves continuous operations, good governance, and diligence supported by the right security strategy of a computer security incident response team (CSIRT) that can protect government operations and control cyber-risks. CSIRT can build better resiliency at the decentralized provincial government level, and contribute to cyber awareness amongst the workforce, public, and other government departments. A CSIRT can further contribute to resilience to the organization by analyzing the threats and attacks, developing countermeasures for the future in protecting its systems and networks from threat actors.
A Comparative Study of CF And NCF In Children's Book Recommender System

Recommender system has played a pivotal role in various fields and scenarios, but there are rare recommender systems for children's books aged 0–12 in China. In this paper, a deep learning named as the Neural Collaborative Filtering (shortly called NCF), is used to predict the list of recommended books for children. By comparing with the other traditional recommender algorithms, such as User-based collaborative filtering (briefly named User-CF) and Item-based collaborative filtering (briefly named Item-CF), it is found that NCF is more suitable for the recommendation of children's books than other methods. NCF has a higher accuracy than others. Through the experiment in this paper, NCF in Hit Ratio (HR) is 0.528 and 0.475 higher than User-CF and Item-CF respectively, in Normalized Discounted Cumulative Gain (NDCG) is 0.543 and 0.473 higher than the last two respectively and in Mean Average Precision (MAP) is 0.550 and 0.475 higher than the last two respectively. Therefore, among the recommender systems for children's books, the NCF model based on deep learning is fitted for the recommended scenes for children's reading, it could be optimized in the next step in further.
Automatic Classification of Government Texts Based on Improved CNN and Skip-gram Models

Aiming at the characteristics of government texts with rich semantic information, the impact of convolutional neural network models on the classification and recognition of government text features is studied, and an optimization method to assist in the classification of official government information resources is proposed so that government staff can better investigate public opinion. Based on machine learning and deep learning theories, this paper conducts an experimental study on automatic classification and recognition of texts using convolutional neural network models and conducts a comprehensive comparative analysis from the perspective of information details and core topic sentences. After data cleaning, Word2Vec is used to construct a word vector matrix, and this information is integrated into the convolutional neural network model for comparison and optimization. And based on the use of a convolutional neural network based on the improved convolutional layer technology, combined with a Skip-gram word vector model, the text is converted into low-latitude feature vectors as the input of the improved convolutional neural network model to achieve the automatic classification of government texts. The experimental results show that the model of this study achieves 92.7% F1 mean and 93.4% ACC in the evaluation of automatic classification of government texts, which not only solves the problem of excessive dimensionality but also significantly improves the execution performance and classification accuracy compared with the traditional methods, and effectively solves the automatic classification of information contents and labels.
A variational joint segmentation and registration framework for multimodal images

Image segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with mutual information (MI) smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results.
Cost and green aware workload migration on geo-distributed datacentres

With the development of the inter-datacentre (inter-DC) virtual machine migration technology, it is possible to reduce the cost of electricity and the environment by using the workload migration across the datacentre. This paper presents a solution - cost and green aware workload migration algorithm (CGWM) that utilising the difference of electricity prices, CO2 emissions and water consumption between different geographical locations to manage the workload. CGWM attempts to reduce electricity costs, carbon emissions and water consumption. When the three optimisation goals conflict, CGWM first to ensure the reduction of electricity cost, and then by adjusting the weight factor to make CGWM more biased to optimise the carbon dioxide or water consumption. Simulation results show CGWM can reduce electricity costs while controlling carbon dioxide emissions and water consumption.
Information Retrieval and Access in Cloud

With increase of information sharing over the internet or intranet, we require techniques to increase the availability of shared resource over large number of users trying to access the resources at the same time. Many techniques are being proposed to make access easy and more secure in distributed environment. Information retrieval plays an important to serve the most reliant data in least waiting, this chapter discuses all such techniques for information retrieval and sharing over the cloud infrastructure. Cloud Computing services provide better performance in terms of resource sharing and resource access with high reliability and scalability under high load.
The Impact of Emergent Technologies in the Evolutionary Path for M-Commerce

M-commerce is a fast-growing opportunity and is acting as an innovative lever for achieving the purpose of increasing sales while better interacting with the clients. Simultaneously, several emerging technologies have appeared in the market and promise to change the current m-commerce paradigm. Therefore, this chapter plans to explore a set of new trend technologies that can plan to build a more efficient relation between the consumer and the m-commerce platform. This study conducted surveys with several market players like marketers and IT leaders to understand their point of view, perceive the relevance and impact of these emergent technologies in m-commerce, identify resistance and challenge points to the proposed change, and look how to allow cohabitation between this new e-commerce paradigm and the traditional physical trade. The main novelty of this study is the inclusion of multiple points of view on the evolution of m-commerce which will allow companies and citizens to perceive the impact of emerging technologies in the future of m-commerce.
Cyclic Codes

Although it will turn out that cyclic codes are not asymptotically good codes, they are an important class of codes which include many useful and widely implemented short length codes, most notably the Golay codes and the general class of BCH codes. BCH codes have a prescribed minimum distance which means that, by construction, we can bound from below the minimum distance and therefore guarantee some error-correction properties. Cyclic codes also provide examples of linear codes with few weights, which allows us to construct designs via Theorem 4.22. The cyclic structure of these codes will appear again in Chapter 10, when we consider p-adic codes.
Assessment of Works in a Foreign Language as a Way to Increase Student Motivation

This article is devoted to the most laborious and creative stage of assessing the work of students - formative assessment, which, in our opinion, significantly increases the motivation of students. With formative assessment, the process of monitoring the success of mastering the material is carried out continuously both by the teacher and by the students themselves. At the same time, it becomes important to organize work in such a way that students themselves become active participants in the educational process. In the search for new solutions for assessing the performance of trainees, the authors came across an original and still relevant system for achieving educational goals, developed by American methodologists, Bloom's taxonomy. This systemis a tool for stimulating the activity of all types of intelligenceand considers assessment as the highest stage of mental activity. When compiling a new home reading manual based on the book of the modern French writer D. de Vigan, the authors decided to plan the stages of each lesson according to Bloom's taxonomy. Traditional for manuals of this kind, activities arranged according to the principle "from reading to speech" are designed to expand the vocabulary of students, encourage them to think about many problems of modern society and instill the skills of mutual and self-assessment.
Multi-Robot Navigation in Unknown Environment Using Strawberry Algorithm

Path Planning focuses on the robot motion from the initial position to final position such that it must avoid the hurdles and finally reach the goal in optimal path. But it is not an easy task because many conditions are included for the efficiency of final result like working on different environments, known or unknown target etc. In this paper the authors have proposed an algorithm inspired by the strawberry plants, and is applied in the path planning. The algorithm can efficiently work for different optimization parameters like Path Length, Energy and Number of Turns. The proposed algorithm is compared with RRT, A-star, PSO and the results obtained are satisfactory. The work can be applied in the real life challenges faced during area exploration
Intelligent Management and Efficient Operation of Big Data

This chapter details how Big Data can be used and implemented in networking and computing infrastructures. Specifically, it addresses three main aspects: the timely extraction of relevant knowledge from heterogeneous, and very often unstructured large data sources; the enhancement on the performance of processing and networking (cloud) infrastructures that are the most important foundational pillars of Big Data applications or services; and novel ways to efficiently manage network infrastructures with high-level composed policies for supporting the transmission of large amounts of data with distinct requisites (video vs. non-video). A case study involving an intelligent management solution to route data traffic with diverse requirements in a wide area Internet Exchange Point is presented, discussed in the context of Big Data, and evaluated.
Methodologies

In this chapter, the authors discuss software development methodologies. These are adaptive process frameworks adjustable to software product size and scope. They usually include a set of methods, principles, techniques, and software development tools. Every methodology can implement any of the lifecycle models. The authors discuss the difference between formal and agile methodologies. The formal methodologies include more artifacts. For each activity, every role assigned to it produces a deliverable. Agile methodologies are applicable in uncertain conditions. These agile methodologies rely on self-disciplined and self-manageable teams. They are more constrained by human-related factors. As in lifecycle models, there is no “silver bullet” in software development methodologies. Due to rigorous processes and more artifacts, formal methodologies are suitable for large-scale product development. Agile methodologies require special techniques and a high level of discipline. Otherwise, they likely result in a low-quality software.
Keyword Expansion Techniques for Mining Social Movement Data on Social Media

Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords. Yet, systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate 5 diverse keyword expansion techniques (or pipelines) on 5 representative social movements across 2 distinct activity levels. Our results guide researchers who aim to use social media keyword search to mine data. For instance, we show that word embedding-based methods significantly outperform other, and even more complex and newer, approaches when movements are in stagnation while being less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random.
SFNet: Stage Spatial Attention and Feature Waterfall Fusion for Mathematical Document Text Line Detection

Text line detection is an important technique in layout analysis. Due to the existence of non-text areas and the change of formula height, text line detection in mathematical documents has always been a huge challenge. In view of the complexity of the spatial structure of mathematical text lines, on the one hand, based on the existing attention mechanism, we proposed a composite network structure with Stage Spatial Attention, which has better feature extraction. In addition, a multi-scale feature fusion structure, called Feature Waterfall Fusion, adds features with low-resolution but stronger semantic information into the features with high-resolution but weaker semantic information indirectly and directly. Combining them results in SFNet. On the other hand, to explore the mathematical document text line detection, we constructed a dataset named Mathematical Document Text Line Dataset (MDTL-1850), which includes about 10,669 text line annotations in 1,850 images (1,280 for training and 570 for testing). We assembled SFNet and a learnable post-processing Progressive Scale Expansion (PSE) surpassed mainstream text detection networks such as PSENet and PANet in all metrics on MDTL-1850. Precision <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(P)$</tex> , Recall <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(R)$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> , which improved by 1.0, 0.7 and 0.9 respectively. The experimental results strongly proved the superiority of SFNet network in detecting text line of mathematical documents.
Fluidra SA, Spain

Kernel pruning methods have been proposed to speed up (simplify) convolutional neural network (CNN) models. However, the effectiveness of a simplified model is often below the original one. This letter presents new methods based on objective and subjective relevance criteria for kernel elimination in a layer-by-layer fashion. During the process, a CNN model is retrained only when the current layer is entirely simplified by adjusting the weights from the next layer to the first one and preserving weights of subsequent layers not involved in the process. We call this strategy progressive retraining, differently from kernel pruning methods that usually retrain the entire model after eliminating one or a few kernels. Our subjective relevance criterion exploits humans’ ability to recognize visual patterns and improve the designer’s understanding of the simplification process. We show that our methods can increase effectiveness with considerable model simplification, outperforming two popular approaches and another one from the state-of-the-art on four challenging image datasets. An indirect comparison with 14 recent methods on a famous image dataset also places our approach using the objective criterion among the most competitive ones.