Reviewerly/exHarmony huggingface dataset:
Securing Database Backups Using Blockchain and Peer-to-Peer Network

Databases are one of the critical services provided by a cloud environment. Database downtime can have a devastating impact on any running service. Database backups are generally made robust with high availability by replicating them in a new server. However, storing a database backup on a new server is not cost-effective. At the same time, storing the backup in the same server does not ensure high availability. In this paper, we propose creating a secure database backup architecture using a transaction blockchain and peer-to-peer network to record the history of the transactions. Our architecture makes database backups highly available with a 4 percent decrease in throughput.
High-fidelity tracking of the evolution of multilevel quantum states

The method of quantum tomography, which allows us to track with high accuracy the evolution of multilevel quantum systems (qudits) in Hilbert spaces of various dimensions is presented. The developed algorithms for quantum control are based on the use of the spinor representation of the Lorentz transformation group. In the simplest case of one-qubit states, it turns out that, in addition to three-dimensional rotations on the Bloch sphere, one can introduce four-dimensional Lorentz pseudorotations, similar to the transformations of the special theory of relativity. We show that feedback through weakly perturbing adaptive quantum measurements turns out to be capable of providing high-precision control of the quantum system, while introducing only weak perturbations into the initial quantum state. It turns out that, together with the control of a quantum system through its weak perturbation, the developed algorithms for controlling the evolution of the state of a quantum system can be super-efficient, providing a higher measurement accuracy than any standard POVM (PositiveOperator Valued Measure) protocols. The results of the study are important for the development of optimal adaptive methods for quantum states and operations controlling.
Automatic Text Summarization with Categorization on Online News About Indonesian Public Figures Using Fuzzy Logic Method

Mass media has a main purpose to provide actual information and lifestyle in society. For that purpose, mass media has been developed into many forms with online news as one of them. With so many news articles on the internet, it's been difficult for readers to get information about an event or a person in a short time and efficiently. Text summarization is one solution to save time and improve efficiency by shortening the document into a shorter version but containing important information from the source document. This research’s goal is to create an automatic text summarization in several news articles in mass media that have been categorized regarding Indonesian public figures using Fuzzy Logic approach. Automatic text summarization can be used in a variety of news articles which are structured texts with neat grammar to help users find the desired information from the many news articles that are available. The result showed automatic text summarization with categorization could provide summarized news articles that are easy to understand, contain the main information of the article, grouped into matched categories and public figure, and worked efficiency.
Some Observations on the Behaviour of Federated Learning

In recent years, there is a rapid growth in artificial intelligence (AI) and its machine learning (ML) techniques. The machines started to take decisions depending on the data that they have, and they started to learn from their mistakes. Artificial intelligence faces two big obstacles today. One is that data resides in most industries in isolated form, and the second one is to improve data privacy and security. To overcome these challenges, there is a concept called federated learning (FL). Google introduced the concept of FL in 2016, and it seems to be a promising technique of machine learning for preserving local data privately by training the model without transfering the data to a central location and operating on distributed data. So in this chapter, we discuss FL definition, architecture, and its type.
Local-to-Global Deep Clustering on ApproximateUniform Manifold

Deep clustering usually treats the clustering assignments as supervisory signals to learn a more compact representation with deep neural networks, under the guidance of clustering-oriented losses. Nevertheless, we observe that, without reliable supervision, such losses for global clustering would destroy the locally geometric structure underlying data. In this paper, we propose a local-to-global deep clustering method based on approximate uniform manifold (LGC-AUM) to address this issue in a two-stage fashion. In the local stage, an intra-manifold preservation loss is proposed to preserve intra-manifold structures locally on basis of approximate uniform manifold, and an inter-manifold discrimination loss is for global inter-manifold structure. Thus, this stage serves to learn more discriminative structure-preserving features by reducing the correlations between different manifolds, which paves the way for the final clustering. Build off the learned features, the second stage explores a clustering loss based on approximate uniform manifold to establish stable network training for effective clustering with two auxiliary distributions. Experiments on five benchmark datasets verify the efficacy of our LGC-AUM as compared to several well-behaved clustering counterparts.
Design of a Remote Meeting Real-Time Attendance System Based on the YOLO Algorithm

Aiming at the problems of poor performance and inaccurate identification of the current real-time attendance system for remote conferences, a design method of a real-time attendance system based on the YOLO algorithm is proposed. First, the hardware configuration of the system is optimized to improve the performance of real-time clock-in monitoring and ensure the stability of the system. The system software function and operation flow are optimized to improve the system operation efficiency and recognition accuracy. Finally, the experiment proves that the real-time attendance system based on the YOLO algorithm can better guarantee the system performance and precision in practical applications and fully meet the research requirements.
Information Hiding of Specific Image Region Using Adversarial Examples

Deep neural networks have been widely used in our daily life. However, malicious organizations may collect image data and identify users for commercial purposes. Even worse, criminals may steal private information through image recognition to achieve illegal purposes. As well known, deep neural networks are easily attacked by adversarial examples. We propose a range oriented adversarial examples generation algorithm to fool AlexNet by improving the non-directional DeepFool algorithm. Furthmore we combine it with object detection and propose an information hiding algorithm of specific image region. Experimental results show that compared with traditional methods such as mosaic, blur, and partial occlusion, our scheme is more concealed while ensuring the usability of the image.
Impact of Political Marketing on Voters: The Conceptual Framework

Political marketing, particularly analyzing voter behavior, has evolved into a very significant tool to develop successful political brands. This study will help political candidates, their parties, the government, and other political organizations understand the political marketing variables that impact voters' wants and preferences. This study is based on exploratory research design to identify, evaluate or review key variables that help gauge the impact of political marketing on voters. The study focuses on secondary source of data which is analyzed using qualitative data analysis software ‘NVivo 12 plus’. Given the amount of money and efforts invested in political marketing, it is critical to understand the factors taken into account by voters while taking decisions regarding the party and the candidate to be voted for to bring them round to vote in a certain way.
Asymmetric 3D face model for Speech Language Pathologist applications

This work proposes an innovative approach for the study of the human face and its related gestures and movements, aimed to quantify the analysis of the health professionals. The developed system follows the movements of the face, and then relevant parameters and information related to facial gestures are extracted, which are useful for different healthcare areas (Psychology, Speech Language Pathologist). Most popular face models are designed to represent and simulate symmetric gestures. In this paper, we propose a set of modifications to the 3D Candide-3 model (which consists of asymmetric action units), in order to simulate gestures with asymmetry. Also, a combination technique between robust state-of-the-art facial landmarks and the modified 3D face model is employed for the facial points tracking phase, in order to improve the tracking performance. Encouraging results were obtained in the perceptual experiments performed.
Preprocessing Application for Car Insurance Claim Classification Model

The use of data in various industry sectors becomes a necessity in predicting or deciding on one of them is the use of car insurance data related to claims made by vehicle owners. The data can later be used by insurance companies to be analyzed related to car insurance claims both from the owner's side of the car and from the condition of the car. This paper will discuss the preprocessing data on auto insurance claim data in America with the aim of later making a data model so that it can be used next to see the accuracy of the data processed classification. The results of processing data and data classification can help car insurance companies in deciding a policy or problem that occurs accurately and measurable. The data used in this paper is data that still has a missing value. Therefore, data cleaning is done by cleaning, filtering, and combining these data. The study used the Car Insurance Claim Data dataset downloaded on kaggle's website. The results showed that the JRIP algorithm had the best accuracy of 83.09 percent (before the preprocessing dataset) and 83.14 percent (after the preprocessing dataset was applied) in the 10 fold cross-validation test mode. With an increased level of accuracy, the data can be better used again as an example for forecasting tau as a reference company to trigger something related to the data.
Analysis of Indian News with Corona Headlines Classification

With the advent of the world wide web, the world has seen an explosion in the amount of information that is available online. People stay informed about the national and international affairs through online news which is readily available and portable allowing ease of access. These news pieces tend to shape people’s thoughts and provoke emotions, which may be positive, neutral or negative, without them realizing their effect. The objective of this work is to create a hybrid model that can analyze the overall effect of digital news content in India. The hybrid approach of sentiment analysis encompasses lexicon and machine learning algorithms as well as a self-created scored corpus of corona related words to classify all sorts of headlines. The labelled dataset is used to train decision tree and random forest algorithms. They are evaluated based on their accuracy scores, classification reports and confusion matrices. The results prove that both the algorithms perform well on the dataset and that the Indian media highlighted neutral news the most. This finding can be very useful for the Indian news agencies since they can alter their reporting strategies to create an impact of their choice on the readers’ minds and thus, increase the readership.
Exploring the Synergies between Social Systems and Human Systems Integration

INCOSE International SymposiumVolume 32, Issue S1 p. 140-140 Submission for a Panel at INCOSE HSI 2021 Exploring the Synergies between Social Systems and Human Systems Integration First published: 03 February 2022 https://doi.org/10.1002/iis2.12878Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume32, IssueS1Supplement: INCOSE Human Systems Integration Conference, November 16–19, 2021, Virtual EventFebruary 2022Pages 140-140 RelatedInformation
Method for Predicting Compound Properties in Drug Development Based on Machine Learning

Data on drugs, targets and indications were collected from the database, and gene expression profile data processed by 1309 small drug molecules were collected from the connectivity map. The biclustering algorithm was used to cluster 1309 small drug molecules in CMap, and the intersection of CMap drugs and drugs in the three databases of DrugBank, TTD and DGIdb was taken. The PubChem tool was used to calculate the chemical structure similarity of the drug, and then the Tanimoto coefficient between the two drugs was calculated from the collected drug data. Using Tanimoto coefficients as parameters, support vector machines, naive Bayes and logistic regression were used to construct combined drug prediction models under different positive and negative sample ratios, and the support vector machines were selected as the best model. Use the nearest neighbor recommendation algorithm to build five similarity models and use logistic regression as the integrated learning algorithm to build the integrated model and perform feature screening. The AUROC value of the integrated model constructed using drug target similarity, drug indication similarity, drug chemical structure similarity and drug expression profile similarity was 0.89, and the AUPR value was 0.383. Applying the optimal model to the prediction of paclitaxel combination medication can draw the performance prediction of the combination medication. Preliminary experiments have proven that the predicted drug combination has a better combined synergistic effect.
The Teaching Design of Sequence Limit Based on Modern Education Technology

Sequence limit is the most important and basic concept in advanced mathematics, and it is also the theoretical basis of continuity, derivative, differential and integral calculus. However, due to the high abstraction and rigorous logic, limit cannot be mastered by most students, and many confusions about the application and proof of limit are also bother students. Therefore, this article will analyze and design the main points based on modern education technology in the teaching of sequence limit, thus enable students to quickly grasp the content of sequence limit.
Relations Reconstruction in a Knowledge Graph of a Socioeconomic System

Data quality, completeness, and consistency are crucial for simulation modeling and predictive tasks in socioeconomic systems. Such systems involve heterogeneous entities and their interrelations, which are becoming available only by combining various data sources. In this study, three different sources were combined in a single knowledge graph (KG). It includes an online social network, an online recruitment system, and a financial bank. The constructed knowledge graph is evaluated on link prediction tasks to obtain complete and consistent data. We try to reconstruct links between users and a) socioeconomic statuses, b) organizations they work for, c) job positions. Knowledge graph embedding models and a graph neural network based on Transformer architecture were applied. We get promising results for reconstruction of User-Employer relations ( $$MRR=0.42$$ , $$Hits@10=0.74$$ ), as well as for reconstruction of User-Position relations ( $$MRR=0.59$$ , $$Hits@10=0.88$$ ).
Afterword

This chapter looks across the landscape of learning in the current age of algorithms and so-called ‘artificial intelligence' with a focus on issues raised in the concept of “the master algorithm” around learning models and the future of learning. Pedro Domingos identifies five “scientific” theories of learning algorithms and presents them sequentially and so capable of improvement by the theorist (and he alone). By contrast, in her conversational framework, Diana Laurillard presents four approaches to framing learning models. The authors prefer Laurillard's modelling but believe the fifth dimension of rhizomatic learning needs to be added to her framework in order to enable the learner to take the final decisions on what has been learned and what they will do subsequently, and so produce a learner-centric framework for learning and architectures of participation. They examine several histories of thinking about intelligence as well as long-term views of technology before outlining, briefly, a phenomenonology of learning as the potential countervailing ideas to AI in education.
Imperceptible adversarial attack with entropy feature and segmentation-based constraint

Methods of adversarial attack and defense have attracting increasing attention in the fields of security and protection related applications. However, current algorithms carry out perturbations on entire images and mostly consider their imperceptibility to machines, while does not take their human imperceptibility into account. In this work, we propose a constrained adversarial attack algorithm with both machine and human imperceptibility based on image entropy feature and accurate segmentation. The proposed algorithm has three merits. First, image entropy-based feature for quantifying the imperceptibility of a semantic region is introduced, which is simple yet efficient to implement. Second, in terms of the imperceptibility metric, accurate target regions for adversarial perturbation are obtained based on scene-aware segmentation and merging. Third, a general adversarial attack based on segmentation region constraint is proposed to induce both machine and visual imperceptibility. Experimental results in terms of qualitative and quantitative analysis reflect the effectiveness of the proposed algorithm compared with the state of the arts.
Intelligent Evaluation System of College Students’ Mental Health Based on Mobile Terminal

To improve the mental health intelligent evaluation of college students, a college students’ mental health intelligent evaluation system based on mobile terminals is designed. The system hardware is designed in advance, and then the collected information is analyzed in the system software to obtain intelligent mental health evaluation results to complete the design of a college student mental health intelligent evaluation system based on a mobile terminal. The experimental results show that the evaluation system effectively improves the evaluation accuracy, recall and efficiency and proves that the system can effectively improve the evaluation effect of the system.
Pruned Genetic-NAS on GPU Accelerator Platforms with Chaos-on-Edge Hyperparameters

Deep Neural Networks (DNNs) is an extremely attractive subset of computational models due to their remarkable ability to provide promising results for a wide variety of problems. However, the performance delivered by DNNs often overshadows the work done before training the network, which includes Network Architecture Search (NAS) and its suitability concerning the task. This paper presents a modified Genetic-NAS framework designed to prevent network stagnation and reduce training loss. The network hyperparameters are initialized in a “Chaos on Edge” region, preventing premature convergence through reverse biases. The Genetic-NAS and parameter space exploration process is co-evolved by applying genetic operators and subjugating them to layer-wise competition. The inherent parallelism offered by both the neural network and its genetic extension is exploited by deploying the model on a GPU which improves the throughput. the GPU device provides an acceleration of 8.4x with 92.9% of the workload placed on the GPU device for the text-based datasets. On average, the task of classifying an image-based dataset takes 3 GPU hours.
The process of making BaM (BaFe12O19) samples based on natural iron sand doped with metal (Co-Cu-Zn) using the coprecipitation method

Abstract BaCO 3 with natural iron sand is the basic material for making samples of Barium M-Hexaferrite (BaM) which is doped to make the utilization of BaM more effective by adding metal ions that have almost the same size as cobalt (Co), copper (Cu), and Zinc (Zn) and using the coprecipitation method. The process of making Barium M-hexaferrite is carried out in the advanced and basic chemistry laboratory, FMIPA University of Mataram. In this study, variations in concentration and temperature variations during the calcination process were used, namely (x = 0.0; 0.4; 0.8; 1.0) (T = 200; 600; and 1000 °C). These variations greatly affect the sample, where the greater the concentration and temperature of calcination, the color produced in the sample will be darker and this is by the purpose of the calcination process carried out, namely the greater the temperature at the time of calcination, the more water content in the sample will be. reduced because the solvent in the sample evaporation occurs. In addition to these materials, the solvent used also plays an important role in the sample-making process, namely using HCl37% to dissolve, NH 4 OH 65% so that the deposition process is faster and aquades as a neutralizer.
Multi-domain and Context-Aware Recommendations Using Contextual Ontological User Profile

Recommender Systems (RS) became popular tools in many Web services like Netflix, Amazon, or YouTube, because they help a user to avoid an information overload problem. One of the types of RS is Context-Aware RS (CARS) which exploits contextual information to provide more adequate recommendations. Cross-Domain RS (CDRS) was created as a response to the data sparsity problem which occurs when only a few users can provide reviews or ratings for many items. One of the kinds of CDRS is Multi-domain RS which use user information from at least two domains to recommend items from all these domains. In this paper, we investigate how Contextual Ontological User Profile can be used for making multi-domain and context-aware recommendations. We show the improvement of accuracy and diversity of recommendations while combining CARS with CDRS.