Reviewerly/exHarmony huggingface dataset:
Fracture Animation Procedure Using Crack Patterns Generated by L-System

In this study, an empirical approach toward visually convincing fracture animation is considered for entertainment purposes. We aim to achieve a real-time fracture animation where the user can directly specify the possible geometry of cracks, but the resulting geometry of fragments is realistic. Our procedure comprises four main tasks: generating a crack pattern on a two-dimensional object through the L-system and the Turtle graphics concept, detecting the shape of fragments by the angle-prioritized depth-first search (AP-DFS), and then classifying it by the compression and classification (CC) procedure, and finally generating three-dimensional objects by sweeping. In the first task, the L-system controls topological complexity, such as the number of branches and how frequent a specified pattern can appear, and the Turtle graphics parameters control geometric complexity, such as the distribution of cracks. A rigid body dynamics simulator has been developed, implementing the above procedure to prepare objects for fracture animation. Through the verification by the rigid body dynamics simulator, we confirmed that the developed application could be used for a new fracture animation procedure based on tailored crack patterns.
Verbal vs. Nonverbal Cues in Static and Dynamic Contexts of Fraud Detection in Crowdsourcing

As an important mode of open innovation, crowdsourcing can effectively integrate external resources, enabling enterprises to obtain stronger competitiveness and more benefits at a faster speed and lower cost. However, this mode has inevitable intellectual property protection challenges, especially on contest-based crowdsourcing platforms. Previous studies mostly focused on the protection of the rights of sponsors while ignoring the rights of workers, rarely paying attention to sponsor fraud, which may reduce the enthusiasm of participants and eventually turn crowdsourcing;' into a lemon market. This study proposes several fraud detection models to address this problem on contest-based crowdsourcing platforms. Furthermore, this paper explores and compares the value of four types of information as deception cues in crowdsourcing contexts via data mining technology and machine learning methods. The results benefit participants in crowdsourcing markets and contribute to fraud detection research and open innovation in the knowledge economy.
Clustering of differentials in CRAFT with correlation matrices

CRAFT is an substitution-permutation network tweakable block cipher proposed at fast software encryption 2019 by Beierle et al., which is designed to optimize the efficient protection against differential fault analysis (DFA) attacks. In this paper, the full round differential characteristics for CRAFT block cipher are given. A new method on counting the number of differentials by using correlation matrix is given. We can compute the number of all optimal characteristics or suboptimum differentials with the same input difference and output difference by hand. We explore the multiple differential trails and compute the probability of differential characteristic by using the multiplication of correlation matrices. Our work complements automatic search methods for the best differential with a careful manual analysis. Since the automatic search method is stranded by storage and search space limitations, which will cause a computer to crash as the number of search rounds increases. Thanks to the correlation matrix technique, we are able to find differential distinguishers for 9-round of the cipher with the probability of at least 2 − 40.68 + 2 − 48.60 ${2}^{-40.68}+{2}^{-48.60}$ . Moreover, we can construct differential distinguisher covers more rounds based on the 9-round differential distinguishers. As one of its typical application, we propose the differential characteristics for the full-round CRAFT which ensure that the probability of each round is optimal. Besides, we explore the clustering effect on the full round by exhibiting a class of high probability characteristics for 9-round. In general, we obtain a good understanding of the propagation of differences for CRAFT due to its algebraic structure.
Pengaruh Literasi Ekonomi Terhadap Perilaku Konsumsi Siswa Kelas XII IPS SMA Negeri 1 Kabila Kabupaten Bone Bolango

&lt;p&gt;The purpose of this study was to determine how big the influence of economic literacy in schools on the consumption behavior of class XII Ips students at SMA Negeri 1 Kabila, Bone Bolango Regency. With the number of respondents 60 respondents (students), survey approach. The quantitative research method, with data collection techniques of observasion, questionnaires and documentation, data analysis techniques using multiple regression analysis. The results of the study show that statistical hypotheses are compiled and there is an effect of economic literacy on the three indicators with the T test (simultaneous), namely; social environment (X1), use of limited resources (X2), and benefits and economic transaction costs (X3) of Fcount Ftable (3,510 ≥ 3,16) with a significance value of 0,021 ≤ 0,05 in school on the consumption behavior of class XII Ips students of SMA Negeri 1 Kabila, Bone Bolango district. And the F test (partial) only the social environment variable (X1) has significant positive effect on students' consumption behavior (Y). And the result of coefficient determination(𝑅2)effect is 15.8%, while the remaining 84.2% influenced by other variables.&lt;/p&gt;&lt;p&gt;Tujuan penelitian ini untuk mengetahui seberapa besar Pengaruh Literasi Ekonomi Di Sekolah Terhadap Perilaku Konsumsi Siswa kelas XII Ips SMA Negeri 1 Kabila Kabupaten Bone bolango. Dengan jumlah responden 60 responden (siswa), pendekatan survei, Metode penelitian Kuantitatif dengan tehnik pengumpulan data observasi, angket dan dokumentasi, tehnik analisis data dengan menggunakan analisis regresi berganda. Hasil penelitian mennunjukkan Hipotesis statistik terdapat Pengaruh literasi ekonomi dari tiga indikator dengandi uji T (simultan) yaitu; lingkungan sosial (X1), penggunaan sumber daya yang terbatas (X2), serta, manfaat dan biaya transaksi ekonomi (X3) sebesar Fhitung ≥ Ftabel (3,510 ≥ 3,16) dengan nilai signifikansi 0,021 ≤ 0,05. di sekolah terhadap perilaku konsumsi siswa kelas XII Ips SMA Negeri 1 Kabila kabupaten Bone bolango. Dan di uji F (parsial) hanya variabel lingkungan sosial (X1) yang berpengaruh positif signifikan terhadap perilaku konsumsi siswa (Y). Serta hasil koefisien determinasi (𝑅2) pengaruhnya sebesar 15,8%, sedangkan sisanya sebesar 84,2% dipengaruhi oleh variabel lain.&lt;/p&gt;
Complexity through Translations for Modal Logic with Recursion

This paper studies the complexity of classical modal logics and of their extension with fixed-point operators, using translations to transfer results across logics. In particular, we show several complexity results for multi-agent logics via translations to and from the mu-calculus and modal logic, which allow us to transfer known upper and lower bounds. We also use these translations to introduce a terminating tableau system for the logics we study, based on Kozen's tableau for the mu-calculus, and the one of Fitting and Massacci for modal logic.
Historical Collections and Library Catalogs: Provenance Metadata, Bibliographic Standards and Frameworks, and Catalog Functionalities

The article discusses the importance of custodial history metadata in bibliographic standards, formats and frameworks, and in library catalogs. It analyzes the present situation and proposes some basic solutions that would make it possible for the custodial history metadata to be entered in a standardized way and to be functionally linked to the holdings metadata so that catalog users would be able to search and retrieve information on the most relevant copies in terms of their provenance. Visual simulations of various functionalities based on the search and results display interface of the Slovenian National Library’s catalog are also included.
L-G Graphs and Voronoi Diagrams Based Recognition of Incomplete Objects Using the Standard Six-views: A First Study

The research on recognition of single-multiple objects has significantly matured with many techniques and robust results. The probabilistic recognition of incomplete objects, however, remains an active field with challenging issues related to their degree of incompleteness and associated issues due to shadows, illumination and other visual characteristics. Object incompleteness here means that only a small portion of a known object is visible and not low-resolution or deformation of that object. The employment of various single machine-learning methodologies for accurate classification of the incomplete objects did not always provide a robust answer to all challenging issues or requires a very long training list of variations of each object. In this paper, we present the initial results using Local-Global (L-G) graphs and Voronoi (V) diagrams methodologies associated with machine learning to generate probabilistic matches of objects with varying degrees of incomplete views. The first results with limited use of synthesis of views are promising and have triggered an interest to continue the efforts for recognizing incomplete objects in a progressive way with synthesis of views of segmented regions that compose an object.
ENIGMA - A SECURE FORENSIC DATA SHARING USING BLOCKCHAIN

-A forensic laboratory is often referred to as a crime lab. The forensic laboratory examines evidence such as DNA, fingerprints, used bullet casings, and even tyre tracks to establish if a crime has been committed and who the offender is. For example, everything collected during the examination of victims of sexual assault such as body fluids, hairs, fibers, or anything found on the victim's clothing may be analyzed at a forensic laboratory. The type of analysis will depend on the type of evidence being analyzed. Evidence is the most important part of the investigation to proceed. In some of the cases, we can see that pieces of evidence are being altered or modified, or changed even before it reaches the judiciary system. Because of this, justice won’t be served to the needy and eventually, the crime rate will increase. The project’s goal is to create a blockchain network that takes the forensic report as input from the pathology lab and that tamper-proof forensic report will be sent to the police station securely.
Pondering on the ways to enhance university reputation

The article describes positive experience of the Peoples’ Friendship University of Russia (RUDN University), which has risen significantly in QS World University Rankings, both in the overall assessment and in the subject ranking. The authors provide factual material and give recommendations for a step-by-step strategy for reorganising educational and scientific activities of the university with a view to increasing the quality of education services, which include the allocation of priority areas; application of new forms of work; development of image strategies in the field of scientific activity and cooperation with employers. The study is based on the analysis of the methodology of the world’s leading rankings. It also focuses on the implementation of the road map of RUDN University and other Russian universities. To reveal best practices, the authors break down the activities of RUDN University while implementing the 5-100 programme and other activities with a view to promoting the university in international university rankings and enhancing its reputation. Study results suggest that the most important steps in determining the direction in the development of the university are the selection of priority areas of activity, organisation of new forms of work, the attraction of leading international and Russian scientists in a certain field of activity for cooperation, the support and development of scientific projects, cooperation with scientific, educational, practical and industrial organisations in Russia and the world, development and implementation of a number of image strategies in the field of scientific activity, and working with employers to organise internships and further employment of university graduates.
Color Image Encryption Algorithm Based on High Dimensional Combination Chaotic System and DNA Coding

In order to solve the problem of limited key space and simple chaotic behavior of a single chaotic system, a high dimensional combined chaotic system model based on cosine transform is proposed. By combining one dimensional chaotic systems with weights, the output results are coupled by cosine transform, and then the corresponding high dimensional combined chaotic system is obtained. A color image encryption algorithm based on high-dimensional combined chaotic system and DNA encoding is designed according to the proposed high-dimensional combined chaotic system. The algorithm first uses the sequence value generated by the high-dimensional combined chaotic system to scramble the plaintext image at the bit and pixel levels. Secondly, DNA encoding technology is used to perform dynamic DNA operation on the scrambled image. Finally, the nonlinear component S-box is used to replace pixel values, which further improves the security of the algorithm. The simulation results show that the combined chaotic system has larger key space and better chaotic behavior, and the color image encryption algorithm based on high dimensional combined chaotic system and DNA can resist various common attack methods and has better security.
Research on the Development and Intelligent Informationization of Language Testing and Evaluation System Based on SSH

This paper analyzes the development process of language evaluation system based on SSH. The system adopts B/S mode, uses SSH as the framework, uses Java language and mysql database, and also uses some classic design patterns and JSP, servlet, css, etc. in the development process. Popular technology, all programs that can generate executable files are evaluated using dynamic evaluation technology in software testing. Programs that have errors in the compilation process and cannot generate executable files are transferred to the code repair module for repair, and then repaired again. Compile and process, if executable files can be generated and transferred to the dynamic evaluation module for evaluation, the data is safe and reliable.
Artificial Intelligence in Legal Education under the Background of Big Data Computation

The purpose of legal education is to cultivate professional legal talents, let them master the framework of the current legal system, use legal reasoning, and solve practical problems according to legal logic. In the context of big data (BD), artificial intelligence (AI) technology helps collect a large amount of information and process the information centrally, making the calculation process much simpler. Therefore, we apply AI to the education industry, design a law teaching system based on AI, and conduct a teaching effect test experiment. The experiment shows that the average grade of the class using the artificial intelligence system for law teaching is higher than that of the class using the traditional teaching method, and the grades increase faster, verifying the feasibility of the artificial intelligence teaching system in legal education.
Experimental analysis of high-temperature creep in FV566 steel based on digital image correlation

Digital image correlation (DIC) is an optical measurement method of material strain/displacement based on visible light illumination, which can be used for the measurement of long-term mechanical behavior. In this paper, an experimental method for analyzing high-temperature creep in FV566 steel material based on DIC was independently designed. Aiming at the problems of glass observation window medium refraction and thermal airflow disturbance in high-temperature testing, the corresponding correction methods were proposed to improve the measurement accuracy. Based on the above methods, high-temperature creep tests were carried out on three specimens with different shapes, and the strain concentration area at 600°C was calculated. Then, the influences of shape and other properties on material creep failure, stress distribution, and actual strain were investigated. Finally, the DIC calculation results were analyzed and compared with results of finite element analysis and the final fracture position of the specimen. The three results had a high degree of consistency, which verified that the proposed method can accurately measure and analyze the creep behavior of materials.
Introduction to the Special Issue on Software-Intensive Autonomous Systems: Methods and applications

Identifying self-admitted technical debt (SATD) plays an important role in maintaining software stability and improving software quality. Although existing methods can detect SATD and researchers have identified design debt and requirement debt, an approach to realize multiple classification of SATD, including defect, test, and documentation, is still lacking. In this paper, we combine text generation oversampling and the Convolutional Neural Networks-Gated Recurrent Unit (CNNGRU) model, and propose an approach called SCGRU to classify multiple debt, including defect, test, documentation, design, and requirement. First, SeqGAN-based text generation is employed to generate new samples by learning the original SATD data, thereby increasing the number of SATD samples such as defect debt and reducing data imbalance. Then, we apply the CNNGRU model to refine SATD into multiple classes. An experiment with cross-project identification of 10 projects shows that our approach is more effective than existing methods such as CNN and text mining. The proposed SCGRU approach has strong advantages especially in cases of flawed debt with very unbalanced data such as test debt and documention debt.
Performance analysis of a cloud-based network analytics system with multiple-source data aggregation

Purpose The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters. Design/methodology/approach The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases. Findings Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic. Originality/value Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.
A IMPORTÂNCIA DO PIBID PARA O GRADUANDO: A EXPERIÊNCIA EM DUAS ESCOLAS ESTADUAIS NO MUNICÍPIO DE PALMEIRA DOS ÍNDIOS DURANTE A PANDEMIA DO COVID-19

This study discusses an experience lived in the performance of PIBID biological science, in a 3rd grade class in a state school in the hinterland of Alagoas.It aims to evaluate the adoption of the didactic-methodological tool, the Interactive Didactic Sequence (SDI), for the construction of the concept of Biological Evolution, based on interactions between students.The SDI consists of three steps: individual concept, group concept and general concept, the latter constructed by a representative of each group.At the end of the SDI, there was a comparison of the concept made by the class representatives with the concept presented by the mediators, which in this case were the PIBIDians.The research has a qualitative character and is justified by the need to use methodological tools for the construction of an integrated knowledge, which considers the previous experiences of ours and how they improve with the scientific knowledge given in the classroom.It was evidenced that the concept constructed by the students is close to the scientific concept,concluding that SDI is an important tool to build meaningful knowledge, based on the teaching-learning relationship between teacherstudent.
A Novel Approach Based on Generative Adversarial Network for Interference Detection in Wireless Communications

With the rapid growth of wireless devices, the communication environment gets complex. The detection of interference or unauthorized signals can improve spectrum efficiency, which is a key technology for limited spectrum resources. Traditional detection methods analyze the parameter characteristics of the received signal. But it is difficult to detect interference with the same time and frequency as the original signal by those feature engineering. As a classical problem in deep learning, anomaly detection is usually solved by supervised learning. But a more challenging situation is to detect unknown or invisible anomalies. It means that the number of abnormal samples is insufficient and the data is highly biased toward the normal samples. In this paper, a wireless communication interference detection algorithm based on generative adversarial network (GAN) is proposed. In the semi-supervised learning scenario, the algorithm detects the time-frequency overlapped interference by the reconstruction strategy. The generator adopts the encoder-decoder-encoder architecture. In the training process, the model jointly learns the data distribution of normal samples by minimizing the distance in both the signal space and the latent space. In the inference phase, a large distance metric implies an abnormal sample. Experiments on simulated communication datasets show the superiority of the proposed algorithm.
Sensor based sign language recognition system

Sign language is used as a primary form of communication by many people who are deaf, deafened and non-verbal. Communication barriers exist for members of these populations during daily interactions with those who are unable to understand or use sign language. Advancements in technology and machine learning techniques have enabled development of innovative approaches to translate these sign languages to spoken languages. This paper proposes an intelligent system for translating sign language into text. This approach consists of hardware as well as software. The hardware consists of flex, contact, inertial sensors and SD card module mounted on a synthetic glove, additionally If-Else (Rule based learning) based learning is performed by Arduino nano (Atmega328p) to represent the proposed system. This system is able to recognize static letters, numbers and translating 26 letters from A to Z and 10 numbers from 0 to 9 from the American sign language. The database with the use of alphabet and numbers is prepared, and tested by kNN and CN2 rule inducer, where kNN has shown promising result. This experiment is done by Orange software. Experimental results demonstrate that our system is effective, cheaper and has high classification accuracy as compared to other technology available in market.