Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Big Data Publishing

Data privacy plays a noteworthy part in today's digital world where information is gathered at exceptional rates from different sources. Privacy preserving data publishing refers to the process of publishing personal data without questioning the privacy of individuals in any manner. A variety of approaches have been devised to forfend consumer privacy by applying traditional anonymization mechanisms. But these mechanisms are not well suited for Big Data, as the data which is generated nowadays is not just structured in manner. The data which is generated at very high velocities from various sources includes unstructured and semi-structured information, and thus becomes very difficult to process using traditional mechanisms. This chapter focuses on the various challenges with Big Data, PPDM and PPDP techniques for Big Data and how well it can be scaled for processing both historical and real-time data together using Lambda architecture. A distributed framework for privacy preservation in Big Data by combining Natural language processing techniques is also proposed in this chapter.
Using Grayscale Frequency Statistic to Detect Manipulated Faces in Wavelet-Domain

Manipulating facial images results in negative influences on the social association, with deep generative models. Although many detection methods have been proposed, they have either designed sophisticated neural networks that lack enough interpretability, or found defects specific to one manipulation method. To address this issue, we propose a new approach to explore the defects of fake facial images after wavelet transform and call it GFS (Grayscale Frequency Statistics). First, we utilize Haar wavelet transformation to decompose the image into low-frequency approximation, horizontal detail, vertical detail, and diagonal detail. The GFS of real and fake images exhibit different distribution and forms in these four subbands. We qualitatively analyze these differences and quantify them as weights. Then, these four subband images are used to train four CNNs respectively, and the obtained detection results also verify the differences in GFS. After that, we combine the prediction results of the four CNNs and the corresponding weights to further improve the detection performance. We conduct extensive experiments on 11 datasets generated by various facial manipulation methods, and the superior results show the effectiveness of our proposed approach. Our findings indicate that the fake images generated by the current facial manipulation methods cannot simulate real images in wavelet-domain.
Decentralized Multi-robot Planning in Dynamic 3D Workspaces

We consider the problem of decentralized multi-robot kinodynamic motion planning in dynamic workspaces. The proposed approach leverages offline precomputation on an invariant planning representation (invariant geometric tree) for low latency online planning and replanning amidst unpredictably moving dynamic obstacles to generate kinodynamically feasible and collision-free time-parameterized polynomial trajectories. Simulation results with up to 10 robots in dynamic workspaces composed of varying obstacle densities (up to $$30\%$$ by volume) and speeds (up to $$2.5\,\mathrm{m/s}$$ ) suggest the use of the proposed methodology for real-time kinodynamic replanning in dynamic workspaces.
PENGEMBANGAN MEDIA UPIN UNTUK MENINGKATKAN HASIL BELAJAR SISWA KELAS IV SD

The purpose of this research is to develop UPIN (Smart Ladder Snake) media products and to find out the improvement of student learning outcomes using UPIN (Smart Ladder Snake) media on the material of ethnic and religious diversity in my country in grade IV SD. The type of research used is research and development (R&amp;D) with a model developed by Sugiyono. The research was carried out at SD Negeri 3 Sumbermanjing using the subject of 15 grade IV SD students for the 2019/2020 school year. The techniques and instruments used in this study were interviews, observations, questionnaires, tests, performance, documentation. Based on the results of the research conducted, the following conclusions were obtained. Learning outcomes using 3_realm that is cognitive aspects with a percentage increase in learning outcomes of Indonesian language learning content by 60% while science subject content is 60%, affective aspects with a percentage increase in learning outcomes by 83%, psychomotor aspectswith a percentage increase in learning outcomes by 60%. Thus it can be concluded that the UPIN (Smart Ladder Snake) media on the material of ethnic and religious diversity in my country in grade IV SD can improve student learning outcomes.
On Higher-Order Probabilistic Subrecursion

We study the expressive power of subrecursive probabilistic higher-order calculi. More specifically, we show that endowing a very expressive deterministic calculus like G\"odel's $\mathbb{T}$ with various forms of probabilistic choice operators may result in calculi which are not equivalent as for the class of distributions they give rise to, although they all guarantee almost-sure termination. Along the way, we introduce a probabilistic variation of the classic reducibility technique, and we prove that the simplest form of probabilistic choice leaves the expressive power of $\mathbb{T}$ essentially unaltered. The paper ends with some observations about the functional expressive power: expectedly, all the considered calculi capture the functions which $\mathbb{T}$ itself represents, at least when standard notions of observations are considered.
Radar Target Tracking Based on Firefly Intelligent Optimization Particle Filter Algorithm

Aiming at the problem of poor tracking accuracy in radar target tracking derived from particle filter (PF) under unknown measurement noise environment.. In this research, the basic particle filter algorithm is improved, and the Firefly Algorithm (FA) is brought in the particle filter in order to displace the original resampling process, so that the particles move on to the high-likelihood area, avoiding the screening of particles, thereby overcoming particle poverty. It also improves the particle quality.In the modeling process, the measurement noise variance is estimated in real time using moving window technology to further ameliorate the correctness of radar target tracking. Finally, by simulating radar target tracking, the availability of the algorithm is proved.
MIDI usage for modeling and animation in Houdini

The hands-on workshop for computer graphics professionals introduces the technique and practical implementation of the usage of MIDI devices in Side FX Houdini software as a relatively easy way to supercharge creative workflows. This technique can significantly improve speed and quality of modeling, animation, and creative iteration for Houdini professionals, especially technical artists, and game artists. It can also present the alternative to the random procedural asset generation or work in conjunction with it and improve results from procedural generators. The workshop covers the brief history of MIDI, practical advice on how to choose the appropriate controller for the specific design needs, and overview of built-in Houdini MIDI workflows and presents a customized approach for creating Houdini digital assets for MIDI mapping in Python.
A Blockchain Privacy Protection Method Integrating Attribute Encryption and Group Signature

Aiming at the problem of privacy protection during data storage on the blockchain, a blockchain privacy protection method combining attribute encryption and group signature is proposed. Firstly, the concealment of the attributes in the access policy is realized through attribute encryption and group signatures to prevent privacy leakage in the policy. Secondly, the traditional attribute encryption scheme is improved, the calculation of the key is completed by the node in the blockchain, which reduces the calculation burden of a single authorization center, and designs a combination of on-chain and off-chain storage to store data. Reduced resource overhead to a certain extent. Finally, the proposed scheme and related algorithms are analyzed in terms of anti-tampering, privacy protection, etc. The results show that the scheme can not only hide user identity, effectively protect identity privacy and transaction privacy, but also has a lower computational cost to satisfy data Security requirements for access.
Ecological And Geographical Education Of Students In The Context Of Sustainable Development

This article considers the features of the development of ecological and geographical education in educational institutions by the example of the Republic of Tatarstan (Tatarstan). As is known, at the present stage of development of society, the quality of the environment is one of the main factors that determine the standard of living and health of people, the possibility of forward movement along the path of sustainable development of the territory, its competitiveness and attractiveness. In Tatarstan, significant attention has been paid to the problems of environmental education of students since the second half of the twentieth century, and since 2001 the republic has been a region for the practical application of the principles of an international document – the Earth Charter, and a project “Tatarstan is a territory of sustainable development and a culture of peace” is being developed. In 2007, the textbook "Earth Charter" and methodological recommendations "Earth Charter" Tatarstan were developed and published for teachers of natural sciences in secondary schools; great attention is paid to the practical implementation of the principles of sustainable development of the human community based on respect for nature, economic justice, tolerance and culture. The results of the study show that in recent years there has been a decrease in the stability of natural complexes under the influence of anthropogenic factors on the environment, especially around large industrial cities and urban agglomerations.
English Chinese Translation System Based on Feature Extraction Algorithm

In order to solve the problem that the semantic context of English is not perfect, the optimal feature selection process does not reach the optimal translation solution, and the translation accuracy is low in the traditional interactive English translation system, an interactive English Chinese translation system based on feature extraction algorithm is designed. This paper selects the optimal translation solution by citing the λ feature extraction algorithm, and constructs the language mapping model for the interactive optimal translation of English Chinese translation. The simulation results show that the designed Interactive English Chinese translation system based on feature extraction algorithm can get the optimal translation solution.
Antifraud Model For Internet Loan Using Machine Learning

Nowadays everyone has moved to online. In the same way frauds are also increased on internet. Although there are many approaches to detect these kind of frauds, they are not accurate. This has to be enhanced This research work make use of random forest and XGBOOST algorithms. This research work uses a big public loan dataset, such as that from Lending Club, to detect fraud. A random forest is used to fill in the missing values initially. The most discriminating features are then chosen using the XGBoost algorithm. Such a basic and successful model could improve the use of machine learning for detecting the frauds in Internet loans, which would help financial engineers.
Feature Integration Through Semi-Supervised Multimodal Gaussian Process Latent Variable Model With Pseudo-Labels for Interest Level Estimation

This study presents a novel feature integration method for interest level estimation using a semi-supervised multimodal Gaussian process latent variable model with pseudo-labels (semi-MGPPL). Semi-MGPPL is an extended version of the multimodal Gaussian process latent variable model (mGPLVM). It integrates features calculated from multiple modalities to predict the users’ interest levels in content. It is known that reflecting known interest levels of known users in the latent space effectively improves the accuracy of interest level estimation. However, previous methods have difficulty reflecting the interest levels when the number of samples is insufficient. Semi-MGPPL efficiently reflects interest levels in the latent space by pseudo-labeling of unlabeled samples and increasing the number of available pairs among labeled samples. In addition, obtaining behavior features is difficult for a new test sample. However, requirement of features of all modalities by previous mGPLVM-based methods makes the calculation of latent variables of a test sample challenging. Semi-MGPPL solves this problem by training a projection function from the original feature to the latent space. The experimental results on real data demonstrate the effectiveness and robustness of semi-MGPPL.
Template Attacks Based on Two-dimensional Convolutional Neural Networks

In the TA(Template Attack) of the side channel attack, the template construction method has been developed from the original Gaussian distribution model to the current machine learning model, such as SVM(Support Vector Machines), CNN(Convolutional Neural Networks), and so on. The traces has not been pre-processed which contains noise, jitter and other interference factors, 1D-CNN(One-dimensional Convolutional Neural Networks) often require many energy consumptions curves to train templates with an attack success rate of more than 90%. This paper proposes a new type of 2D-CNN(Two-dimensional Convolutional Neural Network) structure for template attacks based on the existing CNN1D. The premise of this network structure is to perform SFFT(Sparse Fast Fourier Transform) on the data set, and then, train the pre-processed multi-dimensional data through a CNN2D. Result shows that the CNN2D uses a template trained with less traces to achieve the same attack success rate as the original CNN1D.
Question answering model construction by using transfer learning

Every day there is a need for querying different kinds of data for a number of purposes and tasks. Therefore it became very important to have a tool like a Question Answering System (QAS) for information retrieving from any form of data in a quick manner. In this work a QAS which combines major fields of modern research topics: Deep Learning (DL), Natural Language Processing (NLP) and Information Technology (IT) is developed. Primary goal of this system is to provide users with an appropriate answer, based on the information that it has. Also an implementation of transfer learning on pre-trained bidirctionl nodr for question answering tasks is researched. Developed model is focused on question answering and was trained on a special Stanford dataset for QAS. The work explains the model’s structure, workflow and implementation in a detailed manner.
An active contour model algorithm combined with anisotropic diffusion filtering and global pre-fitting energy

Traditional local active contour models mainly depend on the data fitting term to obtain image edge information, which are sensitive to the position of initial contour. In fact, setting an unreasonable initial contour will result in a time-consuming image segmentation process and difficult evolution at weak boundary. To solve this problem, we come up with a second-order edge detection operator which is based on the global gradient image information to replace the traditional data fitting term, and to obtain edge information with higher speed and accuracy. In addition, the anisotropic edge enhancement method has been introduced to improve the robustness to the weak boundary. In this way, an active contour model which is insensitive to initial contour position is proposed to achieve a faster and more accurate image segmentation result.
The Impact of Voice Assistant Home Devices on People with Disabilities: A Longitudinal Study

The impact of technological innovations in our lives has never been greater, with positive effects on individuals, society, and organisations. The use of assistive technology combined with artificial intelligence has resulted in new smart devices, such as voice assistants (VA). The literature shows that VA devices are helping and providing new opportunities for people with different disabilities. Conversely, empirical studies that understand the impact of VA technology on individual and collective well-being are still scarce. Thus, by conducting a series of longitudinal studies within the ecosystem of physically and visually impaired people, our study aims to respond to this void in the literature. Over a period of 30 weeks, we have carried out 5 longitudinal studies, and collected data from semi-structured interviews, informal conversations, observations and focus group with participants, as well as secondary data collected from the VA devices’ reports. Results from primary data show five emergent themes: ‘the impact of technology on disabilities and well-being’, ‘technology challenges and further improvements’, ‘the relationship between human and technology’, ‘digital surveillance and data protection’, and ‘the need of technological inclusiveness’. Further supportive and confirmatory results from focus group and secondary data were found and discussed in the article.
CMRD-Net: An Improved Method for Underwater Image Enhancement

Underwater image enhancement is a challenging task due to the degradation of image quality in underwater complicated lighting conditions and scenes. In recent years, most methods improve the visual quality of underwater images by using deep Convolutional Neural Networks and Generative Adversarial Networks. However, the majority of existing methods do not consider that the attenuation degrees of R, G, B channels of the underwater image are different, leading to a sub-optimal performance. Based on this observation, we propose a Channel-wise Multi-scale Residual Dense Network called CMRD-Net, which learns the weights of different color channels instead of treating all the channels equally. More specifically, the Channel-wise Multi-scale Fusion Residual Attention Block (CMFRAB) is involved in the CMRD-Net to obtain a better ability of feature extraction and representation. Notably, we evaluate the effectiveness of our model by comparing it with recent state-of-the-art methods. Extensive experimental results show that our method can achieve a satisfactory performance on a popular public dataset.
Unreported Fe and Pb-Cu Slag Heaps in Populonia Hinterland (Tuscany): Application of a Quick Geochemical and Topographical Approach

This paper describes a topographic and geochemical quick field survey approach for the investigation of Fe and Pb-Cu slag heaps deriving from the smelting of Fe-oxides and of base metal sulfides. In particular, it focuses on the Pb-Cu Schiumai site, which is still undescribed despite its importance as one of the main slag deposits in the peripheral area of Campiglia Marittima mining district.This study joins together a quick field survey method and the geochemical analyses (HH-XRF) of slags and ore samples. The obtained data provide a characterization concerning: (i) the extension of the heaps, (ii) the type and chemistry of the slags and ore minerals, (iii) the connection between mining and smelting sites. The results increase the knowledge of the distribution of metallurgical sites in the Populonia hinterland area and allow to reconstruct the connection between proximal/distal mining sites and smelting sites. Additionally, the field survey has highlighted the presence of allochthonous hematite probably traded from the ore deposits of Elba Island and linked with the Fe slags.
ICT and Interactive Multimedia in Teaching 3D Sculpting Design

This research has focused on how information and communication technologies and interactive multimedia have transformed the field of education through the application of interactive multimedia resources oriented to learning three-dimensional design and sculpting through the use of Z Brush. The methodology is based on three phases: Analytical, Creative, and Executive. A quantitative and qualitative approach is presented with the use of surveys and interviews as research techniques. The surveyed group were the students of the Graphic Design School at the Technological University Israel in Quito, Ecuador. The survey´s main goal was to determine the student´s knowledge of handling Z Brush to create an interactive educational product that could help students learn this software. Experts in 3D design and sculpting were interviewed in order to know the feasibility of this project. In addition, with the prototype of the product, a focus group was held with the students. Finally, it is concluded that student learning involves improving their learning pace by incorporating ICTs in the classroom, thereby developing new skills and competencies in the construction of knowledge, managing high results, educational quality and opening new possibilities in 3D sculpting learning and teaching.
Comprehensive Study of MERN Stack - Architecture, Popularity and Future Scope

The biome of JavaScript is constantly evolving and a new framework or library is launched on a regular basis claiming better features. This study focuses on giving guideline to the reader in the process of choosing the best technology by comparing the runtime performance of the MERN (MongoDB, Express, React.js and Node.js) and MEVN (V stands for Vue.js) stack as well as increasing their workability in the job market. To fulfill the purpose, an experiment was conducted to conclude how swift the said stacks perform in building a single page application. For the experiment, two easy to-do applications are built with MERN and MEVN and loading time, adding time, updating and deleting time of the tasks are measured. Moreover, to be able to reveal the software stack trend among the Swedish-based companies, a survey study was conducted. Out of approximately 70 companies contacted, 12 responded. Due to the low number of response on the survey drawing conclusions from the survey and generalizing, the result was challenging. However, the results gathered show that all the respondents use either Vue.js or React.js or both as their client-side software though they are not always combined with Node.js and MongoDB. Other preferred server-side software that are used in combination with React.js or Vue.js are Java, Go and Django. Some of the main factors that the respondents pointed out that affects their choice of software was the ease of learning, the community behind the software, clients’ need and availability of that specific software developer.
Improved dual-frequency phase-coding fringe projection method for 3D shape measurement

The phase-coding method is useful in 3D shape measurement due to its low sensitivity to surface contrast and camera noise. However, as the number of fringe periods increases, the wrapped phase recovered from the sinusoidal phase-shifting patterns is always misaligned with the fringe order calculated by the stair-coding patterns. To solve this, an improved dual-frequency phase-coding fringe projection method is designed to realize absolute phase retrieval. Specifically, the auxiliary fringe order determined by phase-shifting is used to get the absolute phase, which can eliminate fringe-order errors. Moreover, to increase speed and the number of codewords, this paper presents a scheme that only requires seven patterns, including a uniform gray image, two high-frequency sinusoidal fringe patterns, two low-frequency sinusoidal fringe patterns and two phase-coding fringe patterns. Therefore, this method requires fewer patterns to realize high-resolution measurement than existing methods. The success of the proposed method can be proved by experiments.