Reviewerly/exHarmony huggingface dataset:
Education Reform in Mexico: Teachers’ Struggle for a Grassroots Educational Project

Education reform in Mexico held teachers responsible for education’s ills. Evaluation, as part of this reform would be, therefore, the means to overcome lagging social development by professionalizing educators, choosing the best teaching profiles, as well as becoming the path by which to reach quality, which is to be understood as the ultimate achievement. However, in the aftermath of implementation of the teacher evaluation system, education does not seem to have improved; quality has been linked to labor precariousness, and the teaching profession has been “depedagogized” and commodified. Progressive Mexican educators have not remained passive before these processes, developing a current whose purpose is to reclaim their labor, to educate in autonomy and according to their own cognitive necessities, to challenge the educational project in order to break away with neoliberalism’s commodifying logic, and, in turn, to enable a kind of education for the full development of the people and the building up of democratic societies. By taking this path, Mexican educators have recognized themselves as organic intellectuals of the disenfranchised classes, opening up the necessary spaces to theorize and discuss the pedagogical foundations of what education ought to represent for social emancipation. From practice, reflection, and the reading of text and context, as Paulo Freire asserts, they make contributions that emerge as the collective intelligence of the teachers’ movement against education reform and help enrich the foundations of popular education that breathe life into the projects carried out in Mexican schools. Mexican democratic teachers’ contributions to envisioning a more hopeful horizon for the wretched of the earth are summed up in an education that trains for a new civilizing rationality that does not revolve around capital accumulation but rather life and democratic citizenry; cognitive, linguistic, cultural, and gender diversity; the decolonization of colonial knowledge and the recuperation of indigenous peoples’ ancient worldviews; the holistic and full development of personality; the emancipation from all forms of oppression; the critical and intellectual thinking of subjects; happiness and (nonreligious) spirituality; and the development of disruptive skills necessary to redirect the alienating goal of digital technologies toward new usages, such as learning, communication, and empowerment technologies (LCET) in favor of the oppressed.
Research on the Middle Platform Service System of Battlefield Data Governance Information based on 5G Technology

In order to enable fast and agile battlefield data service support capabilities, orderly advance the construction of military big data, and accelerate the implementation of digital battlefields, this paper makes full use of 5G technology and service center integration ideas to build a battlefield data governance information service system. Network slicing technology is used to guarantee differentiated network transmission capabilities for different data requirements, cloud-edge coordination technology is used to guarantee real-time computing power for distributed battlefield data, and service capability opening technology is used to guarantee agile data customization services in complex and changeable environments. New technologies are systematically integrated to achieve value-added empowerment of different operational application data, and provide fast and flexible digital service guarantee for the battlefield.
Group Models of Artificial Polynomial and Trigonometric Higher Order Neural Networks

Real-world financial data is often discontinuous and non-smooth. Neural network group models can perform this function with more accuracy. Both polynomial higher order neural network group (PHONNG) and trigonometric polynomial higher order neural network group (THONNG) models are studied in this chapter. These PHONNG and THONNG models are open box, convergent models capable of approximating any kind of piecewise continuous function, to any degree of accuracy. Moreover, they are capable of handling higher frequency, higher order nonlinear, and discontinuous data. Results confirm that PHONNG and THONNG group models converge without difficulty and are considerably more accurate (0.7542% - 1.0715%) than neural network models such as using polynomial higher order neural network (PHONN) and trigonometric polynomial higher order neural network (THONN) models.
Agents Oriented Genetic-K-Means (AOGK) System for Plagiarism Detection

In the last decade, the plagiarism phenomenon has widely spread and become a topical problem in the modern scientific world, caused by the wide availability of electronic documents online and offline. This work will be devoted to describe a new plagiarism detection system named AOGK « Agents Oriented Genetic-K-means » based on a multi-agents architecture composed of three modules: text parsing to transform documents into vectors; Learning module using genetic algorithms to build a prediction model; Test module using k-means for the final classification of suspicious document; To evaluate their system the authors have used a range of reference metrics (precision, recall, f-measure and entropy) and the benchmark PAN 09. They have compared the results obtained with the performance of other systems found in literature; the authors' aim is the preservation of copyright.
A Classification Framework for Data Mining Applications in Criminal Science and Investigations

The importance of data analysis across different domains is growing day by day. This is evident in the fact that crucial information is retrieved through data analysis, using different available tools. The usage of data mining as a tool to uncover the nuggets of critical and crucial information is evident in modern day scenarios. This chapter presents a discussion on the usage of data mining tools and techniques in the area of criminal science and investigations. The application of data mining techniques in criminal science help in understanding the criminal psychology and consequently provides insight into effective measures to curb crime. This chapter provides a state-of-the-art report on the research conducted in this domain of interest by using a classification scheme and providing a road map on the usage of various data mining tools and techniques. Furthermore, the challenges and opportunities in the application of data mining techniques in criminal investigation is explored and detailed in this chapter.
Towards a Group Decision Support System

This research focuses on the design and development of an interactive multi-criteria group decision support system (GDSS). This system comprises a set of agents communicating through messages. They aim to find an adequate area that can best address certain criteria among a set of variants (e.g. localization of new infrastructure, shopping mall, hospital, etc.). A GDSS is designed and relies mainly on the presentation of the territory through the functionalities of geographic information system (GIS), and the representation of the multiplicity and diversity of decision makers through the functionalities of a multi-agents system (MAS). Moreover, an argumentation-based negotiation protocol MAS module is provided. This approach is justified by the argumentative nature of the process by which participants justify their choices in order to reach a collective decision. This is achieved by exploiting the multi-criteria methods with an analytic hierarchy process (AHP) and Electre III.
Line Breaking

Line breaking, word wrap, or text justification problem often relates to the task of text justification into a page with a given width. Despite a type of text alignment (left, right, justified), the text needs to be broken into lines such that any line does not exceed a page width.
Particle Filtering

We now have all the ingredients in place to describe particle filtering at a sufficient level of generality: on one hand, the chosen Feynman-Kac model defines the recursive quantities we wish to approximate; on the other hand, importance sampling and resampling gives us the right tools to actually compute these approximations.
Model and Method Development for Determining the Completeness of Information for Remote Detection of Landmarks for Autonomous Mobile Robots

The object of research is the completeness of information for making a navigation decision by an autonomous mobile robot when it performs a task in an unfamiliar area without GPS. It is difficult to identify a landmark in the absence and abundance of information. One of the most problematic places is the mathematical description of the criterion according to which an autonomous robot makes a decision about the completeness of information. The paper substantiates a model and method for determining the completeness of information by a robot equipped with several landmarks detection tools operating on different physical principles. It is shown that the implementation of the method requires a priori information on the probability of detecting various landmarks by passive and active means against a continuous and discontinuous background at different illumination of objects, in day and night conditions under different weather conditions. The values of the probability of detecting a specific type of landmark obtained in such studies serve as the basis for constructing an information cadastre for a job performing tasks on the ground. Three formulas are proposed for determining the coefficient of completeness of information, taking into account a priori and a posteriori inventories, and recommended areas of application. The value of this coefficient depends on the threshold level of the probability of detecting a landmark. The reliability of a decision made by a robot is greatest when it is made under conditions of a certain level of completeness of information. The proposed method can be used for other technical objects from which the measurement information is received. Compared with the known methods, it expands the boundaries of application and reveals the possibility of assessing the completeness of information in constantly changing conditions. Along with a change in these conditions, the characteristics of the completeness of information also change. The coefficient of completeness of information can approach unity even in the absence of separate means of detecting landmarks, and then the method makes it possible to assess the need for their use in the given conditions.
Linear Rule Based Ensemble Methods for the Prediction of Number of Faults

Software fault predictionLinear rule based ensemble methods modelsSoftware Fault Prediction (SFP) are highly influenced by the use of learning techniquesTechniques and characteristics of fault datasetsDataset .
UMA ANÁLISE DE ALGORITMOS DE CONSENSO PARA BLOCKCHAIN VISANDO À IMPLEMENTAÇÃO DE SISTEMAS DE INFORMAÇÃO DISTRIBUÍDOS TRANSPARENTES

Transparency in distributed-database information systems is only of real value when integration, sharing, consistency and integrity requirements are all firstly guaranteed. By its turn, the Blockchain technology is considered to be the most promising implementation solution to meet these requirements simultaneously. Within this context, this paper analyzes three important consensus algorithms for the Blockchain technology. This analysis includes a theoretical study and a comparative evaluation as well. The results allow us to better understand the most positive points and vulnerabilities of each analyzed algorithm. Finally, general conclusions and future work conclude this article.
Deep Learning Theory and Software

In the past decade, deep learning has achieved a significant breakthrough in development. In addition to the emergence of convolution, the most important is self-learning of deep neural networks. By self-learning methods, adaptive weights of kernels and built-in parameters or interconnections are automatically modified such that the error rate is reduced along the learning process, and the recognition rate is improved. Emulating mechanism of the brain, it can have accurate recognition ability after learning. One of the most important self-learning methods is back-propagation (BP). The current BP method is indeed a systematic way of calculating the gradient of the loss with respect to adaptive interconnections. The main core of the gradient descent method addresses on modifying the weights negatively proportional to the determined gradient of the loss function, subsequently reducing the error of the network response in comparison with the standard answer. The basic assumption for this type of the gradient-based self-learning is that the loss function is the first-order differential.
Sentiment Analysis of Movie Review Based on LSTM

This paper introduces a sentiment analysis model based on LSTM (Long Short-Term Memory), which can be used for emotional polarity classification of movie review data.The model consists of five levels, namely input layer, embedding layer, LSTM layer, softmax layer, and output layer.IMDb (Internet Movie Database) dataset is used for training, and SGD (stochastic gradient descent), Adadelta, and RMSprop for optimization.The experiment result shows that Adadelta optimization algorithm can get the best training model and the lowest prediction error rate.
The Attitudes of Chinese Organizations Towards Cloud Computing

Cloud Computing become a significant factor in E-commerce and E-business processes and will reduce negative IT impacts on the environment without compromising the needs of future generations. This chapter aim to examine the attitudes of Chinese Organizations towards Cloud Computing adoption. This chapter provides an answer to the question: “What are the advantages and disadvantages of Cloud Computing adoption in Chinese organizations?” The answer was sought by means of an online survey of (N=121) respondents. The survey results revealed the Chinese position regarding the cloud movement, its strengths and weaknesses, the threats posed by Cloud Computing in China, and the specific advantages and disadvantages of this new technology that Chinese organizations and research communities should embrace for the realization of future cloud systems.
Automated Web Service Composition Considering Service Intension and Extension

In service-oriented computing, the way of creating value-added services by automatic composition of existing ones, as a service collaborative pattern, is gaining a great development trend. But most existing methods are based on matching of input and output parameters of services. In fact, there are always other restrictions or conditions on available services that make them not universally applicable. Existing methods did not fully consider this situation, even though this kind of constraint makes a big difference on service composition. To this end, this paper discusses the consideration of service constraints, and defines the concepts of service intension and service extension following our previous work. On this basis, SIDE (Same-Intension Different-Extension) service set is defined, and two algorithms are proposed to solve the problem that SIDE service set is unknown when composing services. This work conducts experiments on a public test set from ICEBE05 to evaluate the proposed methods. The comparison result shows the effectiveness of our algorithms.
An Optimized Image Dehazing Algorithm Based on Local Entropy Theory

Aiming at the problems about color distortion and obvious hierarchical changes of the sky regions in the results of current image dehazing algorithms, this paper proposes an optimized method. We understand the process of image dehazing from the perspective of information gain, and uses the local entropy and brightness features of the image as the main quantitative characterization to recognize the sky area. According to daily life, the sky area in images has the characteristics of small local entropy and high brightness. This conclusion is verified by the analysis of multi-sample images. After obtaining the approximate sky area recognition results, the open operation and the mean filter are used to optimize the sky image. Finally, the image optimized is used as the guide image, and the fusion is performed between the sky image and dehazing image obtained by the traditional method. Compared with the existing methods, the method proposed by us has the following advantages: strong recognition basis and high accuracy; theoretically improved speed; robust. This paper verifies the effectiveness of the method by subjective comparison and objective analysis of four samples using different methods.
Evaluation of the melodic-intonation structure of I.A. Brodsky’s poem “Almost an Elegy” in the 11th grade

The article aims to describe a lesson that represents one of the stages of studying the melodic-intonation structure of poems by I.A. Brodsky in high school. The creation of this method is due to the need to develop an integral concept of school study of poetry, the individual features of which are largely determined by the special sound of the lyricism of the poet’s philosophy-centered creativity. The concept is based on the structural and interdisciplinary approaches to the poetry evaluation, as well as on the works of scholars and teachers who have systematized the experience of studying lyric poetry by schoolchildren, including the sound side of the poetic speech. The analysis of the works of researchers who study the phenomena of melody and intonation of the poetic speech, has aimed at the need to form a multidimensional understanding of I.A. Brodsky’s lyric poetry by schoolchildren. The concept has been developed on the basis of the theoretical, socio-pedagogical and experimental research methods, which allowed to conduct diagnosing, training and control experiments, which revealed an increase in the level of students’ interpretative competence. The step-by-step study of I.A. Brodsky’s poetry in the 11th grade involves schoolchildren’s moving from the intuitive perception of the author’s style to mastering the skills of independent intonation interpretation of the poet’s works, which are based on the ability to isolate the elements from the text of the poem that create its intonation and determine their semantic function. The described lesson implements the second part of the stage of the intuitive perception of I.A. Brodsky’s poetry when the transition is made from an elemental acquaintance with the sound of the poet’s work to clarifying the features of the sound of a particular poem and forming students’ deeper awareness of the correlation between the intonation and ideological interpretations of the poetic work.
Prices

The price of an asset is the amount the buyer pays and seller receives in a transaction. In this chapter, we introduce the student to Yahoo Finance data and show how to perform basic data manipulation techniques. We then show how to compare the capital gains between securities and several common analyses using price data, such as moving average, volume-weighted average prices, as well as how to create candlestick charts and 2-axis charts.
OBSERVING GRBS WITH THE LOFT WIDE FIELD MONITOR

LOFT (Large Observatory For X-ray Timing) is one of the four candidate missions currently under assessment study for the M3 mission in ESAs Cosmic Vision program to be launched in 2024.LOFT will carry two instruments with prime sensitivity in the 2-30 keV range: a 10 m 2 class large area detector (LAD) with a <1 • collimated field of view and a wide field monitor (WFM) instrument.The WFM is based on the coded mask principle, and 5 camera units will provide coverage of more than 1/3 of the sky.The prime goal of the WFM is to detect transient sources to be observed by the LAD.With its wide field of view and good energy resolution of <500 eV, the WFM will be an excellent instrument for detecting and studying GRBs and X-ray flashes.The WFM will be able to detect ∼150 gamma ray bursts per year, and a burst alert system will enable the distribution of ∼100 GRB positions per year with a ∼1 arcmin location accuracy within 30 s of the burst.
A Comprehensive Survey on Techniques Based on TPM for Ensuring the Confidentiality in Cloud Data Centers

Cloud computing is one of the most vital technology which becomes part and parcel of corporate life. It is considered to be one of the most emerging technology which serves for various applications. Generally these Cloud computing systems provide a various data storage services which highly reduces the complexity of users. we mainly focus on addressing in providing confidentiality to users' data. We are proposing one mechanism for addressing this issue. Since software level security has vulnerabilities in addressing the solution to our problem we are dealing with providing hardware level of security. We are focusing on Trusted Platform Module (TPM) which is a chip in computer that is used for secure storage that is mainly used to deal with authentication problem. TPM which when used provides a trustworthy environment to the users. A detailed survey on various existing TPM related security and its implementations is carried out in our research work.
An Applied Mathematical Model for Business Transformation and Enterprise Architecture

The HMM research and development project concept (RDPC) uses factor-driven research and reasoning concept that is supported by a behaviour-driven development environment or a natural language programming that can be easily adopted by any RDPC, where the HMM framework offers such a high level factors editing their logic implementation environment that it can be used by any RDPC researchers without any prior knowledge in computer sciences, technical, or even advanced mathematics. The RDPC is a meta-model that can be used for research topics on enterprise architecture, business transformation or decision-making systems, mathematical models-algorithms. It is supported by many real-life cases. The uniqueness of this RDPC also promotes the future transformation project's unbundling and the alignment of various enterprise resources including services, architecture standards, and strategies to support business transformation processes as the first.
Understanding Genetic Algorithm (GA) Operators Step by Step

In today's world of soft computing, GAs are a hot topic. Researchers developed this fascinating application to face or to counter many difficult problems which cannot be solved through traditional approaches. We have seen that in the published work of GAs, an author does not reveal the working of the GA as a whole. In this chapter, the authors tried to untwist the GA methodology. This knowledge will be helpful in applying GAs for various applications (i.e., in the fields of science and technology and business). In the case of business-related problems, the use of GAs will have viable value. This chapter is a guide to using GAs vs. other soft computing techniques. Later in the chapter, the authors explain the working and comparison of GAs by using question and answer format.