Reviewerly/exHarmony huggingface dataset:
Exploring Python's Powerful Data Collections

One of the powerful data types for programmers that a Python supports is a list. A list is a mutable data structure that holds an ordered collection of items. Lists are useful to work with related data. A tuple is an immutable data type that stores values in a sequence. Slicing can be used to get a range of items. In Python, a set is variable that can store a variety of data, and different data types perform different operations. A set data type in Python is used to store multiple values in a single variable. In Python, dictionary is one of the built-in data types where elements are key: value pairs. In other programming languages, these are called associative memory or associative arrays. Dictionaries are faster in execution, provide easy lookups, and are implemented as hash tables. This chapter focuses on data collection in Python.
Fundamentals of Data Structures

Computer science encompasses various subfields, with data structures and algorithms being important. This chapter aims to enhance proficiency in constructing and analyzing data structures effectively. Studying data structures equips students with the skills to solve real-world problems using suitable structures. The study of data structures offers diverse programming techniques for complex problems yet introduces challenges due to intricate complexities that reshape program architecture. Covering all aspects in a single semester is impractical, as data structures receive extensive attention in graduate, upper-division, and lower-division programs. This text is an introductory resource for the stack, queue, linked list, graph, trees, searching, and sorting algorithms. It also offers insights into their Python implementation and its complexities, applications, and sample code.
The Expression of Hate in Portuguese Digital Media

This chapter aims to analyze the hate speech discourse in the Portuguese context, be it spread or fed through news media platforms and by their users in Portugal. Considering the need to share evidence and produce theoretical and empirical knowledge about this field of research, this study aims to contribute to this reflection and provide information to the international audience. By analyzing the Portuguese legal framework, statistical data and narratives about hate speech against immigrants and other minority groups in digital media (e.g. online news, Facebook) and traditional media (e.g. television, radio), the reader will become better acquainted with the policies associated with hate speech present through digital media and its detection.
Exploring the Role of Python in Self-Supervised Contrastive Learning for Generating Medical Imaging Reports

This chapter investigates Python's involvement in self-supervised contrastive learning (SSCL) for medical imagery with report generation. The research highlights the relevance of SSCL as a method for creating medical imaging reports and the benefits of implementing it using Python. The literature review gives a complete overview of SSCL approaches in medical imaging and shows the advantages of SSCL implementation using Python libraries such as PyTorch, TensorFlow, and Keras. The study's methodology describes the research topics, survey design, methods of data gathering, and analytic procedures. The study named SSCL-GMIR findings indicate that several practitioners utilize SSCL in medical imaging using Python modules. This study highlights Python's significance in implementing SSCL for creating medical imaging report documents, offering researchers and practitioners a more efficient and effective method for producing accurate and informative reports and diagnoses.
Approximation of Hate Detection Processes in Spanish and Other Non-Anglo-Saxon Languages

In this chapter, the authors present how the use of artificial intelligence (AI) can help to identify and reduce the new digital crimes according to hate messages. The appearance of the internet in our lives, at the end of the last century, has meant a great technological advance, providing easier access to a huge volume of information and communication between people. The rise of communication-oriented networks has been such that true digital environments have been created, the so-called social networks, with millions of users all over the planet. This has meant, to a large extent, the modification of our personal relationships, and, unfortunately, the appearance of new ways of sending hate messages. The work presented is aimed at a digital tool built for this purpose for the automatic detection of hate (and non-hate) messages, in Spanish and other non-Anglo-Saxon languages, with AI algorithms, using training data from the Spanish language.
Creating an Online Network, Monitoring Team and Apps to Counter Hate Speech, and Hate Crime Tactics in Europe

In this chapter, the authors present a European initiative for the creation of an online network, monitoring team, and apps to counter hate crime tactics. The project, funded by the Directorate-General of Justice of the European Commission, aims for the promotion of cooperation on reporting and monitoring online hate speech within European countries with a synergy between strong NGOs and universities. The project was based in four axes of correlative actions, that went from the research to the design and implementation of trainings with stakeholders (enforcement bodies and youngsters), the creation of multiplatform tools (web and app), and specific efforts on dissemination and raising awareness with the most vulnerable communities and the general public. The results showed an improvement of the state of art on the situation in the participant countries, as well as real changes in the area of combating hate crime and on-line hate speech in some countries belonging to the consortium.
Ontology of knowledge representation about the prescribed personalized treatment

The paper describes a generalized ontology that makes it possible to form knowledge about various types of treatment (medicamental, restorative, surgical), regardless of the branch of medicine. Ontology is a necessary component for creating an information system focused on solving a class of treatment planning problems. Planning allows you to combine treatments for various diseases and branches of medicine. A method for adapting a generalized ontology to various types of treatment and terminology, which is familiar to the developer of the knowledge base, an expert in the subject area, is described. All specialized ontologies for specific types of treatment are inherited from the generalized ontology, preserving its structure and semantic dependencies. This approach ensures the creation of knowledge bases in decision support systems for the complex prescription of patient treatment, opening up the possibility of creating a single solver, which provides a significant reduction in system development costs. The generalized ontology was developed on the IACPaaS cloud platform and is currently used to create knowledge bases in various fields of medicine.
A Comprehensive Analysis of Stack and Queue Data Structures and Their Uses

The chapter provides a comprehensive and in-depth exploration of two fundamental linear data structures in computer science: Stack and Queue. It begins with an introduction to these data structures, highlighting their key features and fundamental differences. It then explains various operations that can be performed on both data structures, such as push, pop, enqueue, and dequeue. These implementations are thoroughly compared and contrasted, highlighting their advantages and disadvantages. These applications are discussed in detail, showcasing the versatility of Stack and Queue data structures in different scenarios. The chapter includes Python code snippets demonstrating Stack and Queue data structures further to aid the reader's understanding of the material. This chapter provides a comprehensive and practical guide to Stack and Queue data structures, making it a valuable resource for computer science students, researchers, and practitioners.
DeBEIR: A Python Package for Dense Bi-Encoder Information Retrieval

Information Retrieval (IR) is the task of retrieving documents given a query or information need.These documents are retrieved and ranked based on a relevance function or relevance model such as Best-Matching 25 (BM25) (Robertson et al., 1995).Although deep learning has been successful in other computer science fields, such as computer vision with AlexNet (Krizhevsky et al., 2012) and Inception (Szegedy et al., 2014) and natural language processing with transformers (Devlin et al., 2019;Lee et al., 2019;Yang Liu & Lapata, 2019); success in information retrieval was limited due to comparisons against weak baselines (Yang et al., 2019).However, in 2019 (Lin, 2019), deep learning in information retrieval could surpass less computationally intensive keyword-based statistical models in terms of retrieval effectiveness, sparking a resurgence in the field of dense retrieval.Dense retrieval is the task of retrieving documents given a query or information need using a dense vector representation of the query and documents (Lin et al., 2021).The dense vector representation is obtained by passing the query and documents through a neural network.The neural network is usually a pre-trained language model such as BERT (Devlin et al., 2019) or RoBERTa (Yinhan Liu et al., 2019).The dense query vector representation is then used to retrieve documents using a similarity function such as cosine similarity.
IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing

Abstract Achieving efficient and secure sharing of data in the Internet of Vehicles (IoV) is of great significance for the development of smart transportation. Although blockchain technology has great potential to promote data sharing and privacy protection in the context of IoV, the problem of securing data sharing should be payed more attentions. This paper proposes an IoV data sharing scheme based on the hybrid architecture of blockchain and cloud-edge computing. Firstly, to improve protocol’s efficiency, a dual-chain structure empowered by alliance chain is introduced as the model architecture. Secondly, for the space problem characterized by data storage and security, we adopt distributed storage with the help of edge devices. Finally, to both ensure the efficiency of consensus protocol and protect the privacy of vehicles and owners simultaneously, we improve DPoS consensus algorithm to realize the efficient operation of the IoV data sharing model, which is closer to the actual needs of IoV. The comparison with other data sharing models highlights the advantages of this model, in terms of data storage and sharing security. It can be seen that the improved DPoS has high consensus efficiency and security in IoV.
An empirical study of extracting embedded text from digital images

The utilization of images as a means of transferring information is a widespread technique employed to circumvent simple detection functions that primarily focus on analyzing textual content rather than conducting thorough file examinations. This study investigates the efficacy of deep learning models in detecting embedded information within digital images. The data used for analysis was acquired from a secondary source and underwent comprehensive preprocessing. Feature extraction, sequence labeling, and predictive model training were performed using CRNN, CNN, and RNN models. Two specific models were trained and tested in this research: 1) CNN, RNN-LSTM with the Adam optimizer, and 2) CNN, RNN-GRU with the RAdam optimizer for text detection. The findings reveal that Model #1 achieved the highest F1-score during testing, with a score of 98.37% for text detection and 96.73% for word detection. The second model obtained an F1-score of 94.84% and 93.05% for text and word detection, respectively. Model #1 exhibited a word detection accuracy of 98.38% and a text detection accuracy of 96.47%. These findings indicate that the first model outperformed the second model, suggesting that employing RNN-LSTM and the Adam optimizer made a positive impact. Therefore, utilizing deep learning tools and emerging technologies is crucial for extracting textual information and analyzing visual data. In summary, this study concludes that deep learning models can be relied upon to effectively detect textual information embedded within digital images.
Interaction of subjects of the educational Process with the help of a Foresight Project

The article explores the possibilities of foresight technology as a pedagogical tool for predicting the future, competence in the structure of educational experience and assumes that it can be used to create new means of supporting learning technologies and their promotion in the educational process. This is one of the interactive forms of training for specialists of the preschool education system, which allows you to spread innovative pedagogical experience in working with preschool children in relevant areas. The purpose of the work is to study the foresight project method for the possibility of its application by participants of the educational process in preschool educational organizations, since preschool age is the initial stage of a child's awareness of their capabilities, laying the foundations of professional self-determination in the future, acquiring skills in the correlation of society's demands and their own abilities. In the course of the research, a questionnaire was created in five main areas to assess the implementation by heads of children's educational organizations of the use of the foresight project method in their teaching activities. The distinctive features of foresight design, the main problems and relevance of the application of this method by educational organizations are highlighted. Based on the data obtained, methodological recommendations for the use of the foresight project technology in the work on the interaction of subjects of the educational process have been developed. It is proved that the competent application of the foresight method in working with pupils of preschool educational organizations in combination with other modern technologies improves the education process, introduces preschoolers to the world of professions and helps to get the first skills of work.
Less Than Meets the Eye: Simultaneous Experiments as a Source of Algorithmic Seeming Collusion

This article explains the supra-competitive profits observed in Calvano et al.(2020) and subsequent literature. We refute the idea that the phenomenon arises from collusive mechanisms, where each agent exerts the threat of punishment. Instead, our analysis reveals that the high prices and apparent punishment schemes result from simultaneous experimentation and the learning inertia inherent in most reinforcement learning techniques. Additionally, we demonstrate that such seeming collusion can arise in memoryless environments and much faster than previously thought. Our findings challenge the policy implications of prior research and invite new, simpler approaches to address the issue of algorithmic supra-competitive pricing.
M-learning During COVID-19: A Systematic Literature Review

Mobile learning (m-learning) offered opportunities for learning before COVID-19 disease (COVID-19) but experienced a significant surge in usage since learning moved to the homes. This systematic review collates studies published between 2020 and 2021 to understand the impact, challenges, opportunities, and gaps in m-learning during COVID-19. The key findings from the 26 papers reveal the extent to which many families, educators, and governments were caught off guard, some glaring family challenges, and social and digital divides inscribed in technology infrastructure, especially in developing countries. M-learning assumes adequate internet and digital technology infrastructure which means that developing countries continue to be affected the most. A research agenda is created for how m-learning can be better leveraged for future disruptions.
Communication and Technology into Product Design

As social beings, human need interaction to meet their needs in terms of carrying out their lives in daily activities. Human who tends to be individualistic makes the existing sense of empathy eroded, especially in the modern as today. No matter how individualistic a person is, there will be times when humans need help or assistance from others. For example, when someone is experiencing an urgent medical experience or when a crime is being committed. Sometimes, many communication barriers regarding asking for help in these situations. This study is a pilot project that tries to create a jewelry product that has additional functions as a means of communication to ask for help from people around and provide medical information for the emergency needs of the disease suffered by the user of the product. The features developed are jewelry products with designs that can be used to make sounds as a signal to ask for help and the provision of medical information that can be accessed via a QR code that can be scanned using a smartphone device. The method uses a behavioral observation approach and design thinking methodology for the design process. The study and testing of product prototypes show that the sound signal generated from the jewelry product can attract the attention of people around the user and easier to hear. The QR code can be accessed and provide medical information from smartphone devices through the Revive application (mobile or desktop).
Indigenous Knowledge Mobile Based Application that Quantifies Farmers’ Season Predictions with the Help of Scientific Knowledge

This paper presents the development of the indigenous knowledge (IK) mobile based application that quantifies farmers’ season predictions with the help of weather data, satellite imagery data and the Single Exponential Smoothing (SES) model. The system facilitates the indigenous knowledge indicators’ collection and processing to compute farmers’ certainty level of an oncoming rainy season behaviour which is mainly categorized into abundant, droughts and floods. Despite the value of IK indicators, they can only tell the behaviour of the season without stressing on valuable scientific information such as rains onset, distribution, magnitude and cessation. To solve this problem, the researcher integrates the use of IK indicators with farmers’ historic data of periods when they have experienced abundant(normal) rains, less rains (below normal) and excessive rains (above normal). For each period, weather data (rains and average temperature) and satellite imagery data were collected, processed and stored in the database for use by the application. For each satellite image, the following land cover features: vegetation, soil moisture and waterbodies area cover were computed. The indigenous knowledge indicators were also systematically structured to enable certainty level computation of an oncoming season. Based on farmers’ predictions of the oncoming season behaviour, the system extracts historic data with respect to the predicted season and send the data to the SES model. The SES model will predict the next season’s weather data, vegetation, soil moisture and waterbodies cover data to help the farmer to have robust knowledge on the possible season outcome. The data is also downscaled to provide meaning to the farmers.
Archiving 4.0: Dataset Generation and Facial Recognition of DRC Political Figures Using Machine Learning

It is widely recognized that digital archiving represents many advantages compared to manually preserved documents. These include i) reduced risk of losing data, ii) eco-friendliness, iii) data security, iv) faster access to data, v) simple data management, vi) overall costs saving, and vii) potential for data recovery. However, in contrast to developed nations, which have experienced a steady maturity in the field, digital archiving is still in its infancy in developing countries, especially in Africa, where years of slavery, colonization, both economic and political issues, and wars have deprived nations of their history. Building around techniques of the fourth industrial revolution (4IR), this paper addresses this gap by proposing a digital archiving model called “Archiving 4.0”. The model curates a dataset of images of political figures in the Democratic Republic of Congo (DRC) from different sources and classifies these images using machine learning techniques to achieve facial recognition that will help recognize historical people in photographs and videos. To the best of our knowledge, the proposed dataset constitutes the first attempt at digital archiving in the political space of the DRC and provides an example that can be emulated to spin out related works on the African continent where digital archiving is needed for political research studies and also for the preservation of the history of nations. Through performance evaluation, accuracy, precision, recall, and loss; the paper reveals that Transfer learning outperforms traditional machine learning on different metrics of interest when using the generated dataset: 93% of validation accuracy.
Neural Network Strategies and Models for Voice Cloning in a Multi-speaker Mode: An Overview

The evolution of data science and the constant challenge of carrying out different processes using a few resources with simultaneous personalization has promoted interest in the development of voice cloning. Nowadays, different machine learning techniques are used, given their efficiency in generating relationships across multiple parameters. In this regard, we evaluated the best-performing models and the different process optimization strategies within this sector, where through neural network models separated modularly by their functionality, it is possible to generate independent processes taking into account the most significant number of linguistic factors in the generation of the voice, thus obtaining significant results of a clear improvement in the whole process of synthesizing the voice of a target speaker.
A Gaussian process regression-based Noise level Prediction technique for assisting Image Super-resolution

This paper proposes a learning-based image noise level prediction technique based on Gaussian process regression (GPR). The proposed work is divided into three phases: image data processing, feature identification, and the implementation of regression learning model. The developed GPR-based learning model is implemented using an exponential kernel approach. The proposed model is trained, tested, and validated on four publicly available datasets: Flickr 8k, CelebA, Helen, and CAS+PEAL. Additionally, a comprehensive comparison of the performance of various regression learning models, such as linear regression, regression tree, support vector machine, squared exponential GPR, and matern 5/2 GPR, is presented. The experimental results show that the exponential GPR model provides very promising results for the problem of noise level prediction, which can assist in Image super-resolution.