Reviewerly/exHarmony huggingface dataset:
Analysis

Abstract Drawing on sociological Grounded Theory and ethnographic semantics, this chapter argues that analysis is a genuinely creative practice. Analysis entails not simply classifying the data found or produced in the field in accordance with everyday, common-sense knowledge but rather looking for aesthetic and semantic clues in it. It is also not a fixed program, but rather a hermeneutic and explorative search for new connections and patterns of meaning. This is demonstrated through examples of various data materials, such as transcripts of interviews, observation protocols, photographs, video, and material culture.
Blockchain-Based Security Management Platform

As a new generation of information technology, blockchain plays an increasingly prominent role in social operation, bringing significant changes to the economic process, social organization and governance. Blockchain application scenarios are complex and diverse, with many computing nodes, scattered locations and dynamic topology changes. Security threats have become an essential factor restricting blockchain development, and various security problems have become increasingly prominent. The blockchain network lacks an effective security management mechanism, resulting in the frequent flow of illegal data on the chain and malicious attacks, which seriously affects the operation order and healthy development of the blockchain network. Based on trusted computing technology, this paper carries out the research on the blockchain-oriented management platform, constructs a distributed blockchain management center, carries out system management, security management and audit management of the blockchain, and provides a safe and reliable guarantee for the stable operation of the blockchain network.
SAR Ship Detection Based on Attention Pyramids Network for Complex Scenes

As one of the core tasks of ocean monitoring, Synthetic Aperture Radar (SAR) ship detection can provide important clues for military intelligence acquisition and ocean exploration, which plays a vital role in both military and civilian fields. However, the current ship detection method in complex scenes always suffer the inaccurate feature extraction. In this paper, we proposed a new SAR ship detection method for complex scenes based on attention pyramids network(CSAPN). The CSAPN adopts the pyramid structure, and the convolutional block attention modules are applied into each process of multi-scale feature map extraction. In this way, the related features to objects will be enhanced and the unrelated features will be suppressed. The results on real Gaofen-3 SAR data demonstrate the algorithm proposed in this study have the capability to suppress the interferences in complex scenes with a excellent accuracy.
MODERN APPROACHES TO DEFINING THE CONCEPT OF «DIGITAL ECOSYSTEM»

The main approaches to the definition of the concept of «digital ecosystem» are considered.
An intuitive visualization for rapid data analysis

Eye tracking has become a valuable approach in user-centered studies, because it adds an additional source of information that can be recorded for later analysis, instead of just relying on the task completion and the time to completion. Without proper analysis of eye tracking data, important information can remain untold. In this paper, we present an intuitive visualization for rapid data analysis of eye movement patterns. Compared to heat maps or scan path visualizations, AOI-DNAs are easy to implement and take less effort to detect eye movement patterns. Therefore, they are viable for visual data mining and offer capabilities for user interactions for extended visual analytics. In conclusion, we implemented the first prototype to use AOI-DNAs and the first version of a fuzzy search to highlight eye movement patterns. We applied the proposed visualizations in a preliminary analysis to a well-known dataset to illustrate the practical usefulness as a case study in this paper.
Achieving Cinematic Motion with High Dynamic Range

This paper will explore how the appearance of cinematic motion (the characteristic balance of smoothness and realism of motion commonly used in cinema) is affected by high dynamic range (HDR) presentation. We will report on a psychovisual experiment that investigated how observers rated the smoothness of motion between scenes of different motion types (pans, action, slow movement), frame rates, and luminance levels. We found that in order to match the level of cinematic motion in todays' non-HDR presentation, the frame rate may need to either remain at 24 frames per second (fps) or be increased to 30fps, depending on the type of motion and the luminance level of the scene. We conclude that by selecting the appropriate frame rate depending on the motion type in each scene, content creators will be able to achieve the desired cinematic motion without compromising their use of the HDR color palette.
Algorithmic and Advanced Programming in Python - Syllabus in Computer Science, Decision Making &amp;amp; Data - Masterclass 7

This paper is the seventh of a series of 9 lecture notes in Algorithmic and Advanced Programming in Python that are part of the Syllabus of Dauphine PSL's Master in Computer Science, Decision Making & Data. It taught advanced data structure in python with a focus on core and practical details to learn the ins and outs of advanced data structures. In this lecture, you will learn about graph algorithms, topological sort and Shortest Path Algorithms like Dystra
Concluding Remarks

In this effort, we present a comprehensive document on the ZUC stream cipher. ZUC is used in one of the security standards of the LTE architecture (4G) and the New Radio Architecture (5G) of mobile telephony, among many of its applications worldwide (primarily in China).
Quantum Cryptography

Keeping secrets is a universal problem. Quantum technology promises to offer a solution to this problem. Data is all about facts and statistics. Many recognise data as a personal and secret entity. The quantitative data is available in form of numbers while text, images and videos make for qualitative data. Mostly various security mechanisms are put in pace for securing all kinds of data. In general, humans have been using various techniques to keep various secrets intact for centuries. This being a dynamic process, constantly efforts are getting made to upgrade and evolve new security mechanisms.
Photometric Stereo

Shading on a surface provides strong cues for us to perceive the 3D shape of an object. The study of computationally inferring a 3D shape from shading cues originated with shape from shading (Horn in Shape from shading: a method for obtaining the shape of a smooth opaque object from one view. Technical report, 1970, [1], Ikeuchi and Horn in Artif Intell 17(1–3):141–184, 1981, [2]), which aims to estimate a 3D shape from a single image observed under a light source. The problem has attracted many researchers’ attentions since then because of its mathematically rich problem structure and important practical applications. Later, it was shown that, instead of a single image, the use of multiple images observed under different lighting conditions could alleviate the difficulty of the problem of 3D shape estimation from shading cues. This multiple-image approach is known as photometric stereoPhotometric stereo. This chapter describes the problem of photometric stereo and its solution methods.
Web Guard

Malicious content on the web became a global risk at web users, its huge spreading made users vulnerable to all types of cyber attacks that can be performed behind websites. Many recent researches were proposed to detect malicious content. In this work, we propose a chrome extension to defeat the majority types of malicious web content by analyzing URL and page content. The extension is based on a relevant list of malicious features geared with a machine learning classifier also it integrated Google safe browsing for more precision. The obtained experimental results demonstrate the effectiveness of our extension for detecting the malicious content which proved by the perfect result scored on several classifiers with 99.5% accuracy.
Second matrix-matrix semi-tensor product

A new matrix product, called the second matrix-matrix semi-tensor product (MM-2 STP) of matrices is proposed. It is similar to the classical semi-tensor product (i.e., MM-1 STP). First, its fundamental properties are presented. Then, the equivalence relation caused by MM-2 STP is obtained. Using this equivalence, a quotient space is also obtained. Finally, the vector space structure, the metric and the metric topology, the projection and subspaces, etc. of the quotient space are investigated in detail.
A Heuristic Approach for Service Allocation in Cloud Computing

Cloud computing systems are considered complex systems, because of the various classes of services offered for users and the big challenges for providers to meet the increasing demands. Thus, service allocation is a critical issue in cloud computing. Fuzzy modeling is one choice to deal with such complexity. In this paper, the authors introduce a new heuristic service allocation model for cloud computing service allocation. Fuzzy sets are used to determine a candidate cloud for providing a service and crisp sets are used to serve requests from a cloud. Supply and demand are used as the fuzzy input to provide the desired heuristic allocation model for the candidate cloud, and a set of parameters are used to determine a cloud user needs.
A Temporal Filtering Approach Based on Optical Flow Estimation for Video Coding

Video coding uses motion compensated prediction to exploit temporal correlations for compression efficiency. Prior works have demonstrated that substantial coding gains can be achieved by decomposing a long-term reference frame into a synthetic reference-only (non-displayable) frame and an overlay displayable frame that resembles the original frame. The source of the reference-only frame is typically generated by temporal filtering along the motion trajectories across nearby frames, where the motion trajectories are built using block matching algorithms (BMAs), thereby reducing the noise level within this synthetic frame. Noting that the efficacy of the conventional BMAs are limited to capturing translational motion activities, this paper proposes a novel approach that uses a per-pixel motion field generated by an optical flow estimation to form the motion trajectory for more efficient temporal filtering. It is experimentally shown that the proposed method better captures non-translational motion activities, which translates into considerable coding gains for video signals with such complicate motion patterns.
Effects of Multi-Robot Team Formations on Distributed Area Coverage

This paper examines the problem of distributed coverage of an initially unknown environment using a multi-robot system. Specifically, focus is on a coverage technique for coordinating teams of multiple mobile robots that are deployed and maintained in a certain formation while covering the environment. The technique is analyzed theoretically and experimentally to verify its operation and performance within the Webots robot simulator, as well as on physical robots. Experimental results show that the described coverage technique with robot teams moving in formation can perform comparably with a technique where the robots move individually while covering the environment. The authors also quantify the effect of various parameters of the system, such as the size of the robot teams, the presence of localization, and wheel slip noise, as well as environment related features like the size of the environment and the presence of obstacles and walls on the performance of the area coverage operation.
Keystroke Dynamics Authentication in Cloud Computing

Cloud computing needs a strong and efficient authentication system because the user will access his rented part through a faraway connection and it will make the authentication sensor device besides the user place for identification and verification so how to know the user who claimed himself to be the legal user. Keystroke identification system as a biometric authentication technique is strongly Candidate for the security issues in cloud computing technology. Keystroke dynamics as a security system did not need extra hardware because the authentication device will be the existing keyboard based on everyone has a unique style for writing. The other biometric methods are addressed with each advantage and disadvantage along with keystroke method. In this paper, all known studies about keystroke technique are explained and compared between them according to the classification technique, number of the participated users and each study results then introduces a survey on software and hardware of other biometric authentication techniques and after the literature review is addressed then keystroke as a biometric authentication system is suggested to access cloud computing environment because it has many advantages to being a part of the known security systems which spread in our world.
Current Trends in Integrating the Internet of Things Into Software Engineering Practices

The chapter summarizes the concepts and challenges of DevOps in IoT, DevSecOps in IoT, integrating security into IoT, machine learning and AI in IoT of software engineering practices. DevOps is a software engineering culture and practice that aims at unifying software development (Dev) and software operation (Ops). The main characteristic of DevOps is the automation and monitoring at all steps of software construction, from integration, testing, releasing to deployment and infrastructure management. DevSecOps is a practice of integrating security into every aspect of an application lifecycle from design to development.
An Insight Into Deep Learning Architectures

Information retrieval can be visualized as the extraction of the desired information from the flooded resources that spread over world wide web. Image retrieval is the fundamental and critical problem that arises in the retrieval activities. In this regard, it is considered to be a challenging task which requires utmost care. Diverse characteristics of data such as noisy, heterogeneity impose a great barrier over image retrieval applications. This chapter aims to come up with a state-of-the-art approach for overcoming these problems by clubbing together widely recognized deep architecture along with natural language processing. This novel design methodology utilizes the latent query features, deep belief network, restricted Boltzmann machine for learning tasks. This collaborative work can be used to reduce the epoch in the learning periods whereas the rest of the methods fail to achieve the constraints.
Some paradoxes in contemporary upbringing

In historical point of view every human generation learn some standard practices in upbringing from previous generations but create new. In contemporary pedagogy this processes are connected with two basic educational functions – conservative and progressive. Conservatism represent save and continuation of traditional practices in upbringing chiefly in family and educational institutions but according to progressive idea this practices have to be partial or completely changed. This change is important condition for adaptation on children to the present and the future. On the other hand, all this can generate series of contradictions and paradoxes in actions on basic upbringing factors – family and educational institution. In this article the author examine some dysfunctions of educational practices on this factors, as refusal to execute their basic functions, evade responsibility, don’t attach importance to main social problems, inefficient educational practices and so on.
Target tracking algorithm based on improved kernel correlation filtering

Aiming at the problem of severe accuracy degradation in the traditional KCF (Kernel correlation filter) target tracking algorithm in a complex environment, we propose an improved kernel correlation filter target tracking algorithm. First, the target scale is estimated by fusing the scale filter in the DSST (Discriminative Scale Space Tracking) algorithm to solve the traditional KCF algorithm cannot estimate the scale. Secondly, So as to solve the KCF algorithm's limited ability to express the features of the target appearance model, by fusing HOG (Histogram of Oriented Gradient) features and CN (Color Name) features, the algorithm's ability to extract target features is enhanced. Finally, by introducing peak response fluctuation detection, the credibility of the tracking target is judged, so as to decide whether to update the filter model. The experiment tested the overall tracking performance of the improved KCF algorithm by using the video sequences in the OTB2013 and OTB2015 standard data sets. The experimental results show that the accuracy and performance of the improved algorithm in complex environments have been significantly improved.
Research on data analysis of English intelligent assistant teaching system based on BP neural network algorithm

This study applies BP neural network to English teaching assistant system, confirms the parameters of BP neural network, then uses the students’ English test data of a university to train and test the system. The results show that the system can meet the error requirements when the parameters are fixed. Through the performance test of the test set data of the teaching aid system and comparison after the test, the results show that the error generated by the prediction results can meet the requirements of this kind of system.
Security Model for Mobile Cloud Database as a Service (DBaaS)

There's a big change happening in the world of databases. The industry is buzzing about Database-as-a-Service (DBaaS), a cloud offering that allows companies to rent access to these managed digital data warehouses. Database-as-a-service (DBaaS) is a cloud computing service model that provides users with some form of access to a database without the need for setting up physical hardware, installing software or configuring for performance. Since consumers host data on the Mobile Cloud, DBaaS providers should be able to guarantee data owners that their data would be protected from all potential security threats. Protecting application data for large-scale web and mobile apps can be complex; especially with distributed and NoSQL databases. Data centers are no longer confined to the enterprise perimeter. More and more enterprises take their data to the Mobile Cloud, but forget to adjust their security management practices when doing so. Unauthorized access to data resources, misuse of data stored on third party platform, data confidentiality, integrity and availability are some of the major security challenges that ail this nascent Cloud service model, which hinders the wide-scale adoption of DBaaS. In this chapter, I propose a security model for Mobile Cloud Database as a Service (DBaaS). A user can change his/her password, whenever demanded. Furthermore, security analysis realizes the feasibility of the proposed model for DBaaS and achieves efficiency. This will help Cloud community to get an insight into state-of-the-art progress in terms of secure strategies, their deficiencies and possible future directions.
Quantum Information Science Vis-à-Vis Information Schools

Quantum information science (QIS) is a combination of quantum science (which combines radio physics, condensed physics, and electronics) and information science (which combines computer science, information technology, mathematics, information studies, and documentation studies). Quantum information science (QIS) is actually an extension of quantum computing. Quantum information science (QIS) is mistakenly taken as quantum information theory, but it has several differences with this. Quantum information science (QIS) is mainly responsible for improved and faster acquisition, transmission, and processing of information. The 20th century is marked by three monumental achievements, namely, computer science, quantum physics, and information theory, which have not only stunned the civilized world but also ushered into a new world – a new paradigm of science and technology.
Unsupervised Learning Method Via Triple Reconstruction for the Classification of Ultrasound Breast Tumors

Automatic classification of ultrasound breast tumors has been pragmatically mitigating the problem of inconsistency in manual diagnosis. Most analyses have inclined to tackle this task in supervised learning frames, but the need for rich quantities of manual data annotations followed by such methods represents one of the fatal downsides of it. To address this, this study proposes a method of unsupervised learning that uses a triple reconstruction based on encoder-decoder scheme. A customized deep convolutional autoencoder is devised as a principal architecture. The first step of the scenario is to pretrain the network in a generic way. To embed different knowledge in latent space, we then arbitrarily change the target distribution to train two extra networks. Hence, the hybridized latent vector is given to subsequent clustering for sustaining an unsupervised fashion. Experiments on two public datasets, BUSI and UDIAT, outperform the supervised learning-based state-of-the-art methods of both datasets in terms of discrimination, empirically validating the effectiveness of the proposed method. In sum, the proposed method is strictly afforded by unsupervised learning of not requiring any labelled instance in the training phase. Also, the coexistence of the original and additional proposed latent codes allows to produce clustering-friendly distribution which is capable of guiding prominent discrimination performance for ultrasound breast lesions.
Language Models

Because a mathematical generative model is simple, it requires only minimal thinking about the details of the underlying process, allowing us to separate the model from complex phenomena related to word production. Even if such a model obeys the statistical universals of language, however, it is not a trivial task to transform it into a generative language model that can produce a language sequence. For the purpose of language engineering, however, we need a language model.
Three-dimensional visual modelling of geological information of hydraulic engineering based on surface constraint reconstruction

In order to solve the problems of big errors in results and weak spatial expression ability of the existing three-dimensional visual modelling methods for geological information of hydraulic engineering, a three-dimensional visual modelling method of geological information of hydraulic engineering based on surface constraint reconstruction is proposed. Geological information of large-scale hydraulic engineering is loaded into an expert image processing system through the sparse scatterplot analysis method to construct a spatial scatterplot model for geological information distribution; adaptive feature matching is performed to the geological information through the template matching method, and edge contour features of the three-dimensional visual image are extracted through surface interpolation and vector edge clipping method; information enhancement and local profile horizon reconstruction are performed for the three-dimensional reconstruction image about geological information of large-scale hydraulic engineering through the wavelet domain feature decomposition method. Simulation results show that the method proposed in this paper can provide high accuracy in visual modelling of large-scale hydraulic engineering geological information with error of only 0.04 mm, strong spatial expression ability of three-dimensional visual modelling and relatively high matching of feature points, so it can provide good three-dimensional reconstruction effect of geological information.