Reviewerly/exHarmony huggingface dataset:
Research on the Deep Mining of Chinese Language Learning by Internet New Media Information Technology

With the continuous popularization of computers and networks, a new language form of network language has emerged at the historic moment. At present, it is believed that the new media network language has brought a certain impact on the language learning of school students, but the impact on different cities and different regions must be different. The purpose of this article is to study the influence of the new media network language on the development of Chinese language, literature and culture and its significance in the future. This paper uses big data algorithms and in-depth mining algorithms to initially construct a data model that simulates the influence of new media network languages on the development of Chinese language, literature and culture, and sorts out the basic process of the influence of new media network languages on the development of Chinese language, literature and culture. The experimental results show that Interviews and questionnaire surveys were conducted with experimental students. More than 90% of the students believed that the influence of new media network language on the development of Chinese language, literature and culture has a positive significance.
A Novel Filtering Recommendation Algorithm for User Emergency Information Adoption

Emergency case data resources are widely distributed and heterogeneous. At the same time, the command of emergency field needs the cooperation of multiple departments. Therefore, it is urgent to establish an emergency analysis and mining platform, realize the sharing and collaboration of emergency data resources among multiple departments, and assist emergency command and scheduling. According to the actual situation of the current emergency, a similarity measure method (TCRD) is proposed to solve this problem by adding temporal information to reflect information adoption, which integrates user context information and temporal information. Firstly, the temporal information of historical adoption behavior is expressed as a binary coded characteristic matrix, and then the characteristic matrix is mapped into a feature vector by using restricted Boltzmann machine, and finally added to the similarity measurement formula. The improved TCRD method can measure the similarity more accurately, and further improve the quality of emergency information adoption recommendation results.
Resilient Collaborative All-source Navigation

The Autonomous and Resilient Management of All-source Sensors with Stable Observability Monitoring (ARMAS-SOM) framework fuses collaborative all-source sensor information in a resilient manner with fault detection, exclusion, and integrity solutions recognizable to a Global Navigation Satellite System (GNSS) user. This framework uses a multi-filter residual monitoring approach for fault detection and exclusion which is augmented with an additional "observability" Extended Kalman Filter (EKF) sub-layer for resilience. We monitor the a posteriori state covariances in this sub-layer to provide intrinsic awareness when navigation state observability assumptions required for integrity are in danger. The framework leverages this to selectively augment with offboard information and preserve resilience. By maintaining split parallel collaborative and proprioceptive frameworks and employing a novel "stingy collaboration" technique, we are able maximize efficient use of network resources, limit the propagation of unknown corruption to a single donor, prioritize high fidelity donors, and maintain consistent collaborative navigation without fear of double-counting in a scalable processing footprint. Lastly, we preserve the ability to return to autonomy and are able to use the same intrinsic awareness to notify the user when it is safe to do so.
An improved ESN with three reservoirs for time series prediction

In this paper, an improved echo state network with three different reservoirs in serial connection(TR-ESN) is proposed for time series prediction. For obtaining the internal characteristics, The hidden layer of TR-ESN can alternately amplify and extract the input signal. A sufficient condition is given to ensure that the TR-ESN architecture has the echo state property. The gradient descent method is used to optimize eight parameters. Finally, simulation Experiment results show that TRESN can significantly improve prediction accuracy.
Introduction

The purpose of this book is to introduce the concepts that define robotic musicianship and the underlying technology that supports them.
Researching Polyrhythmicity

The present book is based on research into polyrhythmic patterning in music, the arts, contemporary culture and the social sciences. How has this phenomenon been researched, and how might it be researched in future? Multiple-levelled notation of rhythms, showing how one rhythmic sequence relates to others in the same composition or same sociocultural phenomenon, are discussed and demonstrated. Conventional musical notation, though it might provide a starting point for the notation of multiple rhythms, is too bound by the division of time relations into regular time signatures. Issues of timing, terminology and existing models for analysis are explored in order to find a new language for describing and analysing multi-levelled rhythms. Examples of the notation of rhythm are presented, and a new composite and multi-levelled prosody is proposed which operates at macro-, mezzo- and micro-levels in an overall approach to polyrhythmicity.
The Practical Aspect of Using a Combinatorial Model on the Configuration of Combinations

the prACtICAl AspeCt oF usInG A ComBInAtorIAl model on ConFIGurAtIon oF ComBInAtIonsThe paper proposes the practical task of choosing a set of programs to protect information at the enterprise.An optimization combinatorial model is built on the configuration of combinations to solve the task.An algorithm for finding a solution to this optimization problem is presented.A practical example of the use of a optimization combinatorial model and the search for the best choice of a set of programs for data protection at the enterprise is given.
Introduction

This book presents our analysis of intrinsic characteristics of time expressions and named entities, and our use of these characteristics to design algorithms to recognize time expressions and named entities from unstructured text. Regarding time expressions, we find their five common characteristics from four diverse datasets, and according to these characteristics, we propose two methods to model time expressions. The first method is a type-based method termed SynTime, which defines three main syntactic token types to group time-related token regular expressions, and designs a small set of general heuristic rules to recognize time expressions. Our second method is a learning-based method termed TOMN, which defines a constituent-based tagging scheme with four tags, indicating four types of constituent words of time expressions. Essentially, our TOMN scheme overcomes the problem of inconsistent tag assignment that is caused by the conventional position-based tagging schemes. Regarding named entities, we find their three common characteristics from two benchmark datasets, and these characteristics motivate us to design a CRFs-based learning method termed UGTO to model named entities. Like TOMN, UGTO defines another constituent-based tagging scheme with four tags, indicating four types of constituent words of named entities, and the UGTO scheme overcomes the problem of inconsistent tag assignment. When analyzing named entities, we find that named entity recognition is a syntactic task while named entity classification is a semantic task; and the classical task of jointly modeling named entity recognition and classification as an end-to-end task does not improve the performance of the single named entity recognition.
Layered approach of analysing OSS and risk management

In a simple language a software product where in the availability of the source code of the software is available in the public domain is referred to as an Open Source Software (OSS). Some of the models proposed in the papers published in different international journals are discussed in this paper. Some statistical data concerning open source software and the related shortcomings have also been covered. A detailed and a thorough comparison of the OSS with the closed source software, their advantages vis-à-vis the disadvantages and certain other aspects have also been carried out making it simpler and understandable by the readers. Quality assurance being of prime importance in any field is a must and like any other feature it gains the same amount of importance in this field as well. This paper facilitates the readers and researchers to understand and analyse OSS with suitable examples.
Hybrid Segmentation Prototype for Arabic Text-Based Documents

The contribution of this work relates to the field of Arabic text-based document analysis for the detection of plagiarism. This analysis will be carried out according to the triadic computation model of document similarity. The authors propose a hybrid segmentation prototype for Arabic text-based documents that links different processing steps in order to generate the similarity rate between the documents of an Arabic corpus. It involves two segmentation systems and a morphological analysis in order to obtain a matrix representation adapted to the triadic similarity computation according to three abstraction levels: documents, sentences and words.
Corporate Information Security Investment Decisions

This article describes how with information security steadily moving up on board room agendas, security programs are found to be under increasing scrutiny by practitioners. This level of attention by senior business leaders is new to many security professionals as their field has been of limited interest to non-executive directors so far. Currently, they have to regularly report on efficiency and value of their security capabilities whilst being measured against business priorities. Based on the Grounded Theory approach, the authors analysed the data gathered in a series of interviews with senior professionals in order to identify key factors in the context of information security investment decisions. The authors present detailed findings in context of a simplified framework that security practitioners can utilise for critical review or improvements of investment decisions in their own environments. Extensive details for each category as extracted through a qualitative data analysis are provided along with a category network analysis that highlights strong relationships within the framework.
Visual Event Computing II

In this chapter, we will continue event computing within the life cycle, we will emphasize it from the aspect of artificial intelligence in observation, learning, presentation and reasoning. We will critically evaluate the relevant computable algorithms for the purpose of intelligent surveillance.
Basic Visual Cryptography Using Braille

As a significant part of information security, Visual Cryptography (VC) is a secret sharing approach which has the advantage of effectively obscuring hints of original secret. In VC, a secret image is separated into partitions which are also known as VC shares. The secret is only able to be revealed by superimposing certain shares. Since Basic VC is in a structure which is similar to that of Braille where white and black dots are arranged in certain orders, it is feasible to utilize the feature of Braille for the authentication of VC. In this paper, the authors will conduct an experiment embedding Braille into grayscale and halftone images as well as VC shares. The result indicates that the embedding of Braille has little impact on VC secret revealing and enhances the security of VC shares.
Analysis and Research of Power Supply Data Based on MATLAB

This paper mainly introduces an analysis and processing method of power supply data based on MATLAB programming technology under the background of big data. Firstly, the relevant mathematical concepts of the problem are introduced, including the Fourier function in the field of industry and the Bayesian analysis method in the field of statistics. Then the Fourier function is used to calculate the total amount of electricity consumption data in different aspects. Finally, the Bayesian analysis method is used to build a simple modeling in the certain mathematical Interval, and I have be fitting the probability statistics data into the previous conclusion. This paper also gives a part of the visual code and a special visual processing results.
Accurate Classification Models for Distributed Mining of Privately Preserved Data

Data maintained at various sectors, needs to be mined to derive useful inferences. Larger part of the data is sensitive and not to be revealed while mining. Current methods perform privacy preservation classification either by randomizing, perturbing or anonymizing the data during mining. These forms of privacy preserving mining work well for data centralized at a single site. Moreover the amount of information hidden during mining is not sufficient. When perturbation approaches are used, data reconstruction is a major challenge. This paper aims at modeling classifiers for data distributed across various sites with respect to the same instances. The homomorphic and probabilistic property of Paillier is used to perform secure product, mean and variance calculations. The secure computations are performed without any intermediate data or the sensitive data at multiple sites being revealed. It is observed that the accuracy of the classifiers modeled is almost equivalent to the non-privacy preserving classifiers. Secure protocols require reduced computation time and communication cost.
Towards the Development of Smart Spaces-Based Socio-Cyber-Medicine Systems

The concept of Cyber-Medicine System (CMS) is applied to research and development of medical information systems where the Internet is used to integrate medical devices and healthcare services into the system and to connect patients and medical professionals. In this paper, the authors generalize the concept to Socio-CMS, where the social world is added to the fusion of physical and cyber worlds. The social world affects the end-user activity and provides opportunities for collaborative work. A semantic layer is introduced to integrate all system and domain objects from the three digitalized worlds into a smart space: multi-source data, ongoing processes, situation attributes, reasoning rules, and human activity. All objects are dynamically related, leading to such a knowledge-rich structure as a semantic network. Data mining and analytics apply semantic algorithms for this network, including the Big Data case. The derived knowledge feeds construction of advanced healthcare services for supporting medical professionals and for assisting patients.
Deep Learning-based Semantic Analysis of Sparse Light Field Ray Sets

With the emergence of various light field (LF) acquisition systems and of novel techniques for processing and visualizing LFs, end-to-end LF systems start to head for the consumer market. Towards this, the semantic analysis of LFs can play a crucial role in LF processing (e.g. compression, storage and transmission), and in the standardization of LF representation schemes across various use cases. In this regard, we earlier have introduced fristograms as a tool to integrate semantics into LF processing. Fristograms collect sets of rays within a volume of a number of pixels in all 3 directions (horizontal, vertical and disparity) and thus enable semantic analysis based on the ray sets, and consequently semantic processing of LFs. Consequently, fristograms enable the application of filtering techniques considering the underlying characteristic of the scene (e.g. differentiate between Lambertian and non-Lambertian, occluded and dis-occluded regions in the scene). Motivated by the earlier results through statistical analysis of froxels enabling a significant reduction in number of rays while maintaining quality, in this paper, we explore learning-based analysis of froxels. Specifically, we propose to use a deep learning network to classify material properties (such as Lambertian, non-Lambertian, and outliers). Once the classification is done, the LF is filtered semantically. Preliminary results show that compared to the statistical ray analysis of froxels, a learning-based approach can reduce the number of rays even further, yet maintain the visual quality of the LF as measured by well-known quality metrics.
Multiple Data

Recent studies of scholarly data work argue that whatever researchers handle as data and, in particular, what researchers consider as potential evidence for supporting claims, counts as data. In this article I extend the relational approach towards data to the various ways of dealing with data in the course of a single research project. Relying on an example from ecology, I argue that data gain presence for and occupy researchers in manifold ways: for example, as a promise, desire or pressure, or as a problem of trust and strength. In this way we notice that data, in the eyes of researchers, have more than one mode of existence, each of which is linked with its own expectations, challenges and actions. And we notice that these modes of existence not only coexist in the framework of a single project, but can also compete with each other.
Pedagogical ways of military university students’ professional experience development in crisis situations

The paper deals with the role of professional experience in training military specialists to work in modern conditions, associated with the emergence and development of crisis situations that permeate virtually all spheres of society. It is obvious that the existence of any state is unthinkable without professionally trained, qualified and experienced defenders. This increases interest in the development of professional experience of officers as an important component of the readiness for effective action of anti-crisis orientation. The paper shows methodological approaches to the study of the process of military university masters experience development. The author proposes a definition of a military specialist professional readiness on the basis of scientific works analysis. The author also considers distinctive features of the officers professional experience development. The author defines the main ways of professional experience development in crisis situations for military university undergraduates. They combine theory with practice and research activities, the use of international experience and experience of veterans of wars and military conflicts, pedagogical support of students with the value content of education, differentiation of training with a special position of the teaching staff, with special attention to military university students professional experience development.
Crypto currency blockchain and derivatives programming using C++.

C++ is very useful language programming in modeling investment exercises, crypto currency blockchain of the hash rate, financial derivatives and their related Greeks symbols. Each section offers a solid explanation of basic concepts, followed by examples for the reader to work through. Before starting programming, please read Chandan Sengupta (2007), Financial Modeling using C++. Wiley Finance. Good luck in your future career as derivatives programmer, investment programmer, crypto currency developer in the major investment banks such as JP Morgan, Merrill Lunch, Morgan Stanley, Deutsche Bank, Goldman Sachs, and Bank of America. Please e- mail me if you have any questions or if you would like to suggest investment exercises related to digital crypto currencies.My e-mail is guirguismichel@gmail.comThank you for your participation and good luck!
Research on Gesture Recognition Method Based on Image Processing and SIFT-SVM

Aiming at the problem that the accuracy of gesture recognition is not high due to the camera's shooting angle and depth information being affected by illumination, this paper proposes a gesture recognition method based on image processing and SIFT-SVM. First construct a gesture recognition data set, perform image processing such as denoising and segmentation on the collected pictures, then extract the geometric features of gestures through the Scale-invariant feature transform (SIFT) algorithm, and finally use the support vector machine (SVM) to classifies and recognizes different detected gestures. The simulation experiment results show that the accuracy of the method proposed in this paper reaches 99.89%, it has strong robustness, and the recognition speed is faster to meet the needs of general applications.
The Future of Online Education: Possibilities and Limitations in Bangladesh Perspective

This paper aims to show the importance of Online or E-learning in the context of Bangladesh through a survey from respondents’ opinions. Though this study was conducted using a small sample of subjects nearly 150 students and instructors (61% male & 39% female), it significantly highlights the emerging shifting of education towards Online as well as Remote learning specially at tertiary level. The data were mainly collected from the students of a university and a college named German University Bangladesh and Chandana Govt. College respectively. Some officials also helped getting some pedagogical information. The result of this study identified the necessity of Online Learning and its emerging possibilities in Bangladesh. Along with the possibilities, this study finds out some perceptions where the improvement and changes should be done to facilitate the system of online education. However, a significant number of difficulties were found in implementing this new technology in Bangladesh. Furthermore, the results of this research initiated that how the future of online education may shape the country’s overall development structure.
Game-theoretic analysis of encrypted cloud data deduplication

Deduplication has been extensively studied in terms of cloud data security and privacy. However, whether a deduplication scheme can be practically adopted is rarely investigated. Researchers have discussed the impact of economic facts and incentive mechanisms in motivating the acceptance of deduplication, but they either did not deeply explore this issue or failed to propose an effective solution. This chapter employs game theory to capture the interactions between stakeholders in two types of deduplication schemes: a server-controlled deduplication scheme (S-DEDU) and a client-controlled deduplication scheme. We propose a bounded discount–based incentive mechanism in S-DEDU, which attracts data users to participate. We also design an individualized discount–based incentive mechanism for motivating data holders to behave cooperatively and protecting data privacy at the same time. Furthermore, we conduct realistic dataset–based experiments to demonstrate the validity of our incentive mechanisms for motivating the adoption of deduplication. In the end, we summarize this chapter and propose prospective research outlooks.