Reviewerly/exHarmony huggingface dataset:
Pelatihan Shooting Movie Islami Sebagai Media Pembelajaran Siswa SMP Islam Darul Muttaqin

Community service is a medium that bridges the world of education with society where the university sends lecturers and students to the community to mingle and collaborate in dealing with problems that exist in society, especially at Daarul Mutaqin Islamic Junior High School, Metro City. The purpose of this community service is to provide understanding and skills to students and teachers related to the use of Islamic shooting as a medium for student learning to make it easier to understand and remember the values ​​contained in the material presented and the media can be felt by all parties.&#x0D; Keywords: Pelatihan Shooting Movie, Learning Media, Media Pembelajaran
ShufText: A Simple Black Box Approach to Evaluate the Fragility of Text Classification Models

Text classification is the most basic Natural Language Processing (NLP) task. It has a wide range of applications ranging from sentiment analysis to topic classification. Recently, deep learning approaches based on Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformers have been widely used for text classification. In this work, we highlight a common issue associated with these approaches. We show that these systems are over-reliant on the important words present in the text that are useful for classification. With limited training data and discriminative training strategy, these approaches tend to ignore the semantic meaning of the sentence and rather just focus on keywords or important n-grams. We propose a simple black box technique ShufText to present the shortcomings of the model and identify the over-reliance of the model on keywords. This involves randomly shuffling the words in a sentence and evaluating the classification accuracy. We see that on common text classification datasets there is very little effect of shuffling and with high probability these models predict the original class. We also evaluate the effect of language model pre-training on these models and try to answer questions around model robustness to out-of-domain sentences. We show that simple models based on CNN or LSTM as well as complex models like BERT are questionable in terms of their syntactic and semantic understanding.
On the Sixth International Olympiad in Cryptography NSUCRYPTO

NSUCRYPTO is the unique cryptographic Olympiad containing scientific mathematical problems for professionals, school and university students from any country. Its aim is to involve young researchers in solving curious and tough scientific problems of modern cryptography. From the very beginning, the concept of the Olympiad was not to focus on solving olympic tasks but on including unsolved research problems at the intersection of mathematics and cryptography. The Olympiad history starts in 2014. In 2019, it was held for the sixth time. We present the problems and their solutions of the Sixth International Olympiad in cryptography NSUCRYPTO $$^{\prime} $$ 2019. Under consideration are the problems related to attacks on ciphers and hash functions, protocols, Boolean functions, Dickson polynomials, prime numbers, rotor machines, etc. We discuss several open problems on mathematical countermeasures to side-channel attacks, APN involutions, S-boxes, etc. The problem of finding a collision for the hash function Curl27 was partially solved during the Olympiad.
Quantum teleportation by utilizing helical spin chains for sharing entanglement

We develop a new protocol for sharing entanglement (one ebit) between two parties using the natural dynamics of helical multiferroic spin chains. We introduce a novel kicking scheme of the electric field for enhancing the teleportation fidelity in our protocol that works in the presence of an appropriate choice of parameters. We also investigate the effect of a common spin environment causing decoherence in the entanglement sharing channel. We compare the results to that of XXZ and XX models subject to a similar entanglement sharing protocol and find that the helical multiferroic chain with the kicking scheme provides a better singlet fraction. We show that the kicking scheme in conjugation with the optimized parameters enhances the fidelity of teleportation even in the presence of impurities and/or decoherence. The advantage of the kicking scheme shown in the impurity cases is an important result to be useful in a realizable setup of helical multiferroic spin chain.
Online learning of socio-emotional behaviors in primary education during the COVID 19 pandemic. Case study

In this paper we will analyze the characteristics of socio-emotional behaviors of primary school students in online learning conditions, the psycho-educational needs of students and socio-emotional learning, as well as a number of educational intervention strategies (positive teaching, positive discipline, assertive discipline).
An image encryption algorithm based on SM4 and Base64

Abstract In recent years, with the continuous development of international cryptographic standards, China has also paid more attention to the design and development of cryptographic algorithms. To guarantee the security of commercial passwords, China’s National Commercial Password Management Office has formulated a series of cryptographic standards, including SM1 (SCB2), SM2, SM3, SM4, SM7, SM9, Zu chongzhi Sequence Cryptography (ZUC) and so on. Compared with other cryptographic algorithms, the SM4 cryptographic algorithm has higher security features, and its performance of cryptographic indexes can be in line with international standards. It can resist differential attacks, linear attacks and other common attacks, so it has received complete attention and application. Nowadays, the network is becoming more and more developed, and the security problem of image stream data is also becoming more and more serious. Therefore, this paper proposes an image encryption algorithm based on SM4 and Base64, which outputs the RGB values of each pixel into a matrix, and then encrypts and decrypts them using SM4 and Base6 encoding and has higher reliability and more substantial anti-attack capability in the image transmission process.
Modern Technologies in the Process of Determining the Professional Suitability of Freshmen Students of a Teacher Training College

One of the main problems in determining the professional suitability of freshmen in educational institutions is the shortage of signifi-cant social and professional experience among them. The actuality of the topic is caused by the problem of determination of professional suitability of students in teaching profession and planning their future career, as well as the functioning of the modern labor market and meeting the needs of the state in the labour force. The purpose of the article is to analyze the state of professional self-determination of college students, to identify the most suit-able future specialists, to conduct a study of their motivational sphere and enterprising attitude to their activities. The leading method in the research is the method of the socio-psychological attitudes of the individual diagnosis in the need- motivational sphere created by O. F. Potyomkina, also the method of the individual diagnosis on the motivation for the success by T. Ehlers, al-lowing to reveal professional suitability to the teaching career of a of college freshmen. As a result of the study the practical significance which lies in de-termining the students' readiness for the responsible choice of a profession by means of modern technologies, and allows to be a demanded employee in the labor market and develop them professionally and personally was re-vealed.
Cloud services, storage and communications at large scale for reliable enterprise systems

The Cloud digital ecosystems, comprising a myriad of computing paradigms, from Cloud data centres to IoT devices, also referred to as Cloud-to-thing continuum, has become a de facto technological d...
Reciprocal Distance Transform Maps for Crowd Counting and People Localization in Dense Crowd.

In this paper, we propose a novel map for dense crowd counting and people localization. Most crowd counting methods utilize convolution neural networks (CNN) to regress a density map, achieving significant progress recently. However, these regression-based methods are often unable to provide a precise location for each people, attributed to two crucial reasons: 1) the density map consists of a series of blurry Gaussian blobs, 2) severe overlaps exist in the dense region of the density map. To tackle this issue, we propose a novel Reciprocal Distance Transform (R-DT) map for crowd counting. Compared with the density maps, the R-DT maps accurately describe the people location, without overlap between nearby heads in dense regions. We simultaneously implement crowd counting and people localization with a simple network by replacing density maps with R-DT maps. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art localization-based methods in crowd counting and people localization tasks, achieving very competitive performance compared with the regression-based methods in counting tasks. In addition, the proposed method achieves a good generalization performance under cross dataset validation, which further verifies the effectiveness of the R-DT map. The code and models are available at this https URL.
Using a Hybrid Intelligent Information Systems Approach for Advertising Video Generation

In recent years, the effectiveness of traditional banner advertising on the Internet has been declining; users tend to pay less attention to the text and graphic banners. This leads to the fact that advertising agencies are forced to come up with sophisticated mechanics to attract users. The trend of recent years is the development of personalized advertising offers in social networks. In this article, we talk about the architecture of the system we developed for a unique advertising project, the purpose of which is to analyze available information from the user profile in the VK social network and generate a personalized video based on the received data. At almost all stages, we use machine learning methods to extract implicit profile information and filter data. The system architecture was created on the basis of the proven approach of Hybrid Intelligent Information Systems (HIIS).
The ethical academy? The university as an ethical system

Universities may be seen as an evolving network of ethical systems that govern teaching, research, service, and administration. The university system, however, is changing; adding new rules, new wa...
Intestinal region reconstruction of ileus cases from 3D CT images based on graphical representation and its visualization

This paper proposes an intestinal region reconstruction method from CT volumes of ileus cases. Binarized intestine segmentation results often contain incorrect contacts or loops. We utilize the 3D U-Net to estimate the distance map, which is high only at the centerlines of the intestines, to obtain regions around the centerlines. Watershed algorithm is utilized with local maximums of the distance maps as seeds for obtaining “intestine segments”. Those intestine segments are connected as graphs, for removing incorrect contacts and loops and to extract “intestine paths”, which represent how intestines are running. Experimental results using 19 CT volumes showed that our proposed method properly estimated intestine paths. These results were intuitively visualized for understanding the shape of the intestines and finding obstructions.
Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation

Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism. &#x0D;
Adaptive probability hypothesis density filter for multi-target tracking with unknown measurement noise statistics

Under the Gaussian noise assumption, the probability hypothesis density (PHD) filter represents a promising tool for tracking a group of moving targets with a time-varying number. However, inaccurate prior statistics of the random noise will degrade the performance of the PHD filter in many practical applications. This paper presents an adaptive Gaussian mixture PHD (AGM-PHD) filter for the multi-target tracking (MTT) problem in the scenario where both the mean and covariance of measurement noise sequences are unknown. The conventional PHD filters are extended to jointly estimate both the multi-target state and the aforementioned measurement noise statistics. In particular, the Normal-inverse-Wishart and Gaussian distributions are first integrated to represent the joint posterior intensity by transforming the measurement model into a new formulation. Then, the updating rule for the hyperparameters of the model is derived in closed form based on variational Bayesian (VB) approximation and Bayesian conjugate prior heuristics. Finally, the dynamic system state and the noise statistics are updated sequentially in an iterative manner. Simulations results with both constant velocity and constant turn model demonstrate that the AGM-PHD filter achieves comparable performance as the ideal PHD filter with true measurement noise statistics.
Combining an Autoencoder and a Variational Autoencoder for Explaining the Machine Learning Model Predictions

A method for explaining a deep learning model prediction is proposed. It uses a combination of the standard autoencoder and the variational autoencoder. The standard autoencoder is exploited to reconstruct original images and to produce hidden representation vectors. The variational autoencoder is trained to transform the deep learning model outputs (embedding vectors) into the hidden representation vectors of the standard autoencoder. In explaining or testing phase, the variational autoencoder produces a set of vectors based on the explained image embedding. Then the trained decoder part of the standard autoencoder reconstructs a set of images which form a heatmap explaining the original explained image. In fact, the variational autoencoder plays a role of the perturbation technique of images. Numerical experiments with the well-known datasets MNIST and CIFAR10 illustrate the propose method.
Machine Learning based Mechanism for Crowd Mobilization and Control

At present, the world is in a difficult pandemic situation i.e., COVID-19. More than 95 Million cases are found and are increasing every day. Greater than 2 Million people died of the virus. Currently, The United States, India, and Brazil are the top most affected countries, and many countries are working to discover the vaccine. After successive lockdowns, the world is trying to come back to normal life with safety restrictions. So, every place like malls, schools, shops, temples, etc., is mandatory to check the temperature and mask worn by every person. The process is time-consuming and involves large human intervention thus increasing the expenses. Hence a setup is to be developed that detects face masks, measures temperature, and controls the crowd entering the building. To achieve these operations, an IR-proximity sensor, IR-temperature sensor, a barricade system, a microprocessor, and LED indicators are used. The setup is fabricated using low weight, low-cost PVC pipes. At first, the proximity sensor checks whether the person is entering the setup and gives input to the microprocessor Raspberry pi which then opens the 1st barricade. Temperature sensor works on the principle of Stephen-Boltzmann law. The temperature of the person is measured and the pi camera detects the face mask. The face mask detection is done by the microprocessor using Open CV and Tensor Flow by comparing images provided in a dataset enhancing machine learning methodology. The entire process is powered by a 12V DC adapter. The barricade system can be installed at all major public places and the system functions effectively.
RETRACTED: Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.
Dual time- and wavelength-multiplexed photonic reservoir computing

Photonics-based AI processors have the potential to outperform digital AI accelerators in terms of throughput, latency, and efficiency, and meet the growing demands of AI edge computing. One such class of processors, known as photonic reservoir computers, are a promising candidate for performing real-time edge computing, but their performance has been limited by the small number of effective nodes in the reservoir. We discuss our advances in developing a next-generation photonic reservoir with dual time and wavelength multiplexing. This allows for a reservoir with increased size and complexity, enabling it to successfully perform complex classification and prediction tasks.
Quantum supremacy in end-to-end intelligent IT. Pt. I:Quantum software engineering–quantum gate level applied models simulators

Principles and methodologies of quantum algorithmic gates design for master course and PhD students in computer science, control engineering and intelligent robotics described. The possibilities of quantum algorithmic gates simulation on classical computers discussed. Applications of quantum gate of nanotechnology in intelligent quantum control introduced. Anew approach to a circuit implementation design of quantum algorithm gates for fast quantum massive parallel computing presented. The main attention focused on the development of design method of fast quantum algorithm operators as superposition, entanglement and interference, which are in general time-consuming operations due to the number of products that have performed. SW &amp; HW support sophisticated smart toolkit of supercomputing accelerator of quantum algorithm simulation on small quantum programmable computer algorithm gate (that can program in SW to implement arbitrary quantum algorithms by executing any sequence of universal quantum logic gates) described. As example, the method for performing Grover’s interference operator without product operations introduced. The background of developed information technology is the "Quantum / Soft Computing Optimizer" (QSCOptKBTM) SW based on soft and quantum computational intelligence toolkit.
The Quantum Supremacy Tsirelson Inequality

A leading proposal for verifying near-term quantum supremacy experiments on noisy random quantum circuits is linear cross-entropy benchmarking. For a quantum circuit C on n qubits and a sample z ∈ {0,1}ⁿ, the benchmark involves computing |⟨z|C|0ⁿ⟩|², i.e. the probability of measuring z from the output distribution of C on the all zeros input. Under a strong conjecture about the classical hardness of estimating output probabilities of quantum circuits, no polynomial-time classical algorithm given C can output a string z such that |⟨z|C|0ⁿ⟩|² is substantially larger than 1/(2ⁿ) (Aaronson and Gunn, 2019). On the other hand, for a random quantum circuit C, sampling z from the output distribution of C achieves |⟨z|C|0ⁿ⟩|² ≈ 2/(2ⁿ) on average (Arute et al., 2019).In analogy with the Tsirelson inequality from quantum nonlocal correlations, we ask: can a polynomial-time quantum algorithm do substantially better than 2/(2ⁿ)? We study this question in the query (or black box) model, where the quantum algorithm is given oracle access to C. We show that, for any e ≥ 1/poly(n), outputting a sample z such that |⟨z|C|0ⁿ⟩|² ≥ (2 + e)/2ⁿ on average requires at least Ω((2^{n/4})/poly(n)) queries to C, but not more than O (2^{n/3}) queries to C, if C is either a Haar-random n-qubit unitary, or a canonical state preparation oracle for a Haar-random n-qubit state. We also show that when C samples from the Fourier distribution of a random Boolean function, the naive algorithm that samples from C is the optimal 1-query algorithm for maximizing |⟨z|C|0ⁿ⟩|² on average.
Caravel: A New Earthworm-Based Open-Source Development for the Italian Seismic Monitoring System

Abstract The Istituto Nazionale di Geofisica e Vulcanologia (INGV) is in charge of earthquake monitoring and surveillance in the Italian territory as a part of the civil protection system. Technological improvements in the last years were taken into account for developing new protocols and software to upgrade all the procedures in the monitoring centers. Real-time earthquake evaluation consists of phase picks, preliminary and automatic hypocenters, local magnitudes and ground-motion parameters. The real-time analysis system presently in use at INGV is the starting point for a new multitier compound system that relies on four main components: an automatic earthquake detection and location system based on Earthworm; a new seismological relational database for parametric data; a full set of new webservices application programming interface specifications to share information and provide data at the application level and; finally, a set of multiplatform interactive revision tools developed to analyze, store, use, and distribute the seismic parameters in real time. These last three components are being completely developed ex-novo at INGV in Rome. Such a system has been engineered to communicate with the International Federation of Digital Seismic Networks standard webservices as well as custom home-made INGV services. Through its custom embedded features Caravel will allow the INGV personnel on duty for seismic surveillance to evaluate and review all automatic estimations before they are communicated to the Italian civil protection agency and then published by external tools through e-mail, SMS, Twitter, and webpages.