Reviewerly/exHarmony huggingface dataset:
Effect of noise on deterministic remote preparation of an arbitrary two-qudit state by using a four-qudit χ-type state as the quantum channel

We present a protocol for remote preparation of an arbitrary two-qudit state by using a four-qudit [Formula: see text]-type state as the quantum channel via positive operator-valued measurement. We first propose the protocol for remote preparation of an arbitrary two-qudit state via positive operator-valued measurement in noiseless environment and then discuss the protocol in noisy environments. Four important quantum decoherence noise models, the dephasing noise, the qudit-flip noise, the qudit-phase-flip noise and the depolarizing noise, are considered in our protocol. The output states and the fidelities of remote state preparation in four different types of quantum noises are presented. It is shown the protocol for remote state preparation via positive operator-valued measurement with [Formula: see text]-type state has the advantage of transmitting less particles for remote preparing an arbitrary two-qudit state. The fidelities of remote state preparation depend on the coefficients of original two-qudit state and the decoherence rates of the noise models.
Digitalization of School Biological Education: Problems and Solutions

In the article the use of digital technologies for the educational process in biology has been studied. The study has aimed at developing and testing the methodology for developing a block of educational tasks in biology by using digital educational resources. Surveys, testing, and the pedagogical experiment have been used as research methods. It has been determined that biology teachers need the methodology that allows the competent use of digital educational resources in teaching activities. In the article the methodology for developing educational tasks in biology by using digital educational resources has been introduced. It is based on the taxonomy of B. Bloom’s educational objectives. The efficiency of the methodology has been confirmed by the results of testing students in the control and experimental groups: the level of development of students’ universal learning activities (ULA) has considerably increased, and the rate of students with project skills has grown.
Implementation of Anti Forensics on Hard Drives Using the DoD 5220.22 M Method and British HMG IS5 E

The process of securing data is related to anti-forensic science, one of the anti-forensic techniques that can be used to safeguard data security, namely by deleting data on storage media. This study examines the implementation of data deletion using the DoD 5220.22 M and British HMG IS5 E methods, then compares these methods. The comparison of the two methods includes performance tests, forensic tests, and data recovery tests. The results of the performance test show that the two methods are strongly influenced by the anti-forensic tools used and do not provide a significant difference when applied using one of the tools. The results of the implementation of data deletion using both methods on the hard disk drive are declared safe to delete data, as evidenced by the extraction results in the forensic test using the Autopsy tool found files on the partition :F with the number of 252 files and on the partition :I with the number of 1 file and the extraction results from the test Forensics using the Recover My File tool managed to find files with the number of 102 files on different partitions, but all the files found in the forensic test process cannot be accessed. The results of the recovery test show that the safest method in the process of deleting data is the British HMG IS5 E method using the Active @ Kill Disk tool, as evidenced by all the results of the recovery process using three tools that do not find any files. Meanwhile, the application of the deletion method that is generally carried out by users, namely the shift + delete method, is declared unsafe, as evidenced by the results of the recovery tests conducted showing that the deleted files can be recovered 100% and can be reaccessed using recovery tools.&#x0D;
Bibliography Studies of the Russian State Library: History and Present Situation

The Russian State Library has made significant contribution to the development of Russian bibliographic thought, starting from the period of the Great Patriotic War (holding scientific conferences, defending PhD theses on bibliography topics by library employees). The major achievement is the development of problems of recommendatory bibliography: the works by B.A. Smirnova of the 1940s through 1960s, major collective studies conducted in 1967—1989 under supervision of the V.I. Lenin State Library of the USSR on the problems of “Effectiveness of recommendatory bibliography in reading guidance” and “Recommendatory bibliography as means of promoting books and reading guidance”. In 1976—1989, the sector of bibliography theory played the significant role in the development of Russian bibliography (since 1983 — the sector of general problems of theory, methodology and organization of bibliography). The release of the serial collection of scientific works “Voprosy bibliografovedeniya” [Questions of bibliography science] (1976—1990) was of great theoretical and organizing value. In the 1990s, when there were no divisions on bibliography science, a number of monographs and dissertations on bibliographic topics were created by individual Library employees. In the Scientific research department of bibliography, established in 1999, there was organized the bibliography science sector (existed until 2015), where famous bibliographic scholars V.A. Fokeev and B.A. Semenovker worked. Within the framework of the sector, G.L. Levin researched on the problems of the national bibliography of Russia. Studies in bibliography science were also created by the other employees of the Department (S.P. Bavin, A.V. Teplitskaya). In 2001, there were resumed publication of the collection “Voprosy bibliografovedeniya” and the activity of the Dissertation Council, where seven dissertations on bibliographic topics were defended by the RSL employees. At present, both researchers and bibliographers of the Scientific Research Department of Bibliography carry out scientific research in parallel with the compilation activities. The authors of bibliographic publications are also employees of other scientific and library divisions of the Russian State Library. Of great importance is the activity of the Scientific Research Department of Bibliography on the information support of bibliographic science, such as creation of indexes of bibliographic literature and bibliographic production of libraries.
Evaluating a Blockchain-Based Supply Chain Purchasing Process Through Simulation

Tracking financial data is a task that usually involves intermediaries and this issue has not been totally covered by information systems. Blockchain has been presented as a potential solution though. The aim of this paper is to perform a proof of concept of purchasing management that makes use of blockchain technology in order to track financial data. The adopted methodology was simulation to compare two different scenarios: a traditional purchasing process (As-Is) and another using blockchain (To-Be). The output of the simulations was used to compare the transactional costs of each model measured through two variables: the execution time of placing orders and the number of service stations required. It could be verified that the blockchain network alternative had a superior performance according to the type of architecture employed. In the present study, the private blockchain with proof of work adjusted to seek hashes that begins with two consecutive zeros had the best achievement.
Laughter Synthesis: Combining Seq2seq modeling with Transfer Learning

Despite the growing interest for expressive speech synthesis, synthesis of nonverbal expressions is an under-explored area. In this paper we propose an audio laughter synthesis system based on a sequence-to-sequence TTS synthesis system. We leverage transfer learning by training a deep learning model to learn to generate both speech and laughs from annotations. We evaluate our model with a listening test, comparing its performance to an HMM-based laughter synthesis one and assess that it reaches higher perceived naturalness. Our solution is a first step towards a TTS system that would be able to synthesize speech with a control on amusement level with laughter integration.
Technology 4.0 and Digital Transfer Technology -The Competition of Learning Online (E-learning) After A Decade

In 2008, FPT corporation launched the Internet-Violympic Math Competition, the first contest in Vietnam that combines modern educational methods and strengths in IT, helping high school students love and learn more effectively. With nearly 30 million contestants in 63 provinces and cities across the country, the competition has created the joy of learning for tens of millions of students over the past 10 years. Following the success of Violympic, FPT corporation brought the VioEdu Online Education System to the market, contributing to creating fair learning opportunities for students nationwide, promoting the quality of national education. VioEdu is an online training system for elementary, junior high and high school students, helping to personalize their learning, save time and increase learning efficiency by applying the latest technology such as: Artificial intelligence (AI), Big data analysis, graphical knowledge modeling and compatible learning methods. Accordingly, the training process, data on learning behaviors and knowledge of students will be synthesized and analyzed by AI to find strengths and gaps in knowledge to propose appropriate learning pathways. As a result, each skill, depending on the nature, will be cultivated or promoted separately, helping them to learn the right focus and not spread out. The content of the lessons on VioEdu is developed and developed by a team of excellent teachers and educational experts from education departments and universities, following the curriculum of Vietnamese textbooks. In addition, the system also has about 30% of advanced and expanded knowledge. The form of visual lecture presentation, built as a game with encouraging prizes, creates a learning interest for children. Only with computers or mobile devices connected to the internet, students from all over the country can access modern education, shorten the knowledge gap between regions by the VioEdu online education system. Currently, the system has more than 400 video lectures and more than 100,000 content of Math teaching knowledge for elementary students. In the product development roadmap, VioEdu will cover all content of all levels of Vietnamese Mathematics, then extend to Mathematics in English, English, Physics, Chemistry, and STEM. The paper attempts to evaluate the effectiveness of the VioEdu system based on the testing process in the 2018-2019 school year with 2,000 students at some elementary schools in Hanoi, with 73.8% of students making progress. after 3-6 months of studying on VioEdu (according to an independent objective assessment of the teacher directly teaching).
Hierarchical Representations Feature Deep Learning for Face Recognition

Most modern face recognition and classification systems mainly rely on hand-crafted image feature descriptors.In this paper, we propose a novel deep learning algorithm combining unsupervised and supervised learning named deep belief network embedded with Softmax regress (DBNESR) as a natural source for obtaining additional, complementary hierarchical representations, which helps to relieve us from the complicated hand-crafted feature-design step.DBNESR first learns hierarchical representations of feature by greedy layer-wise unsupervised learning in a feed-forward (bottom-up) and back-forward (top-down) manner and then makes more efficient recognition with Softmax regress by supervised learning.As a comparison with the algorithms only based on supervised learning, we again propose and design many kinds of classifiers: BP, HBPNNs, RBF, HRBFNNs, SVM and multiple classification decision fusion classifier (MCDFC)-hybrid HBPNNs-HRBFNNs-SVM classifier.The conducted experiments validate: Firstly, the proposed DBNESR is optimal for face recognition with the highest and most stable recognition rates; second, the algorithm combining unsupervised and supervised learning has better effect than all supervised learning algorithms; third, hybrid neural networks have better effect than single model neural network; fourth, the average recognition rate and variance of these algorithms in order of the largest to the smallest are respectively shown as DBNESR, MCDFC, SVM, HRBFNNs, RBF, HBPNNs, BP and BP, RBF, HBPNNs, HRBFNNs, SVM, MCDFC, DBNESR; at last, it reflects hierarchical representations of feature by DBNESR in terms of its capability of modeling hard artificial intelligent tasks.
Orientation-aware Vehicle Re-identification with Semantics-guided Part Attention Network

Vehicle re-identification (re-ID) focuses on matching images of the same vehicle across different cameras. It is fundamentally challenging because differences between vehicles are sometimes subtle. While several studies incorporate spatial-attention mechanisms to help vehicle re-ID, they often require expensive keypoint labels or suffer from noisy attention mask if not trained with expensive labels. In this work, we propose a dedicated Semantics-guided Part Attention Network (SPAN) to robustly predict part attention masks for different views of vehicles given only image-level semantic labels during training. With the help of part attention masks, we can extract discriminative features in each part separately. Then we introduce Co-occurrence Part-attentive Distance Metric (CPDM) which places greater emphasis on co-occurrence vehicle parts when evaluating the feature distance of two images. Extensive experiments validate the effectiveness of the proposed method and show that our framework outperforms the state-of-the-art approaches.
Convergence Analysis of the Guaranteed Parameter Estimation Algorithm for Models of One-Dimensional Chaotic Systems

This paper considers the parameter estimation problem for models of one-dimensional chaotic systems.The guaranteed algorithm is proposed in the context of set-membership approach, which assumes that only intervals of possible values are known for the uncertain variables in the model (initial condition, parameter and measurement errors).The algorithm recursively computes the interval estimates of the parameter at every time step.If the prior information is correct, found interval estimates always contain the true value of the parameter.For certain models of measurement errors the result of the algorithm is the exact value of the parameter (the final interval estimate contains a single point).The goal of this study is to derive conditions under which the guaranteed algorithm improves the interval estimate of the parameter.
The Management Of Flood Protection System Of Dewa Ruci Underpass In Bali

Dewa Ruci intersection is one point of traffic congestion point in Bali. Dewa Ruci is a major intersection that connects the city of Denpasar, Kuta, Jimbaran, and Nusa Dua. In addressing traffic congestion, built underpass so that traffic becomes smooth when passing through the intersection Dewa Ruci. Part of the Dewa Ruci intersection is below the ground level, so risks of flooding. The source of water that potentially fills the underpass is groundwater seepage and rainfall. The flood protection of the underpass is capable of storing 1344 m3 water and with the pumping system. In this research, the maintenance, operation, and management model of the flood protection system is developed that includes the development of organizational structures and standard operating procedures to ensure that the Dewa Ruci flood protection system works well sustainably.
Deep Learning Approach for Automatic Topic Classification in an Online Submission System

Topic classification is a crucial task where knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery.An application of topic classification is article (e.g., journal/conference paper) classification which is very useful for online submission systems.In fact, numerous online journals/magazine submission systems usually receive thousands of article submissions or even more for each month.This leads to a huge amount of time-consumption of editors to process and categorize the submissions aiming to look for and assign appropriate reviewers to the submitted articles.In this study, we propose an approach based on natural language processing techniques and machine learning algorithms (both classic machine learning and deep learning) to automatic classify the topics of articles in an online submission system.We show by promising performance collected from prediction tasks to present that the proposed method is a potential approach for applying to the real system.
A Combined Method of Two-model based on Forecasting Meteorological Data for Photovoltaic Power Generation Forecasting

Under the background of the continuous development of photovoltaic power generation technology, accurate prediction of photovoltaic output power has become an important subject. In this paper, a combined method of two-model based on forecasting meteorological data for photovoltaic power generation forecasting is proposed. To solve the problem of the adaptability of a single model, two different models are used according to the different types of output power characteristics. The K-means clustering algorithm is used to classify different weather types according to the historical meteorological data. After predicting the irradiance and temperature of the period to be predicted and classifying the period into different types, the photovoltaic output power is predicted by a suitable model. The two prediction models are the Wavelet- Decomposition-ARIMA model and EDM-SA-DBN model, which are suitable for periods with larger and smaller fluctuation amplitude of photovoltaic output, respectively. Wavelet decomposition can refine the data with large fluctuations on multiple scales, make the data smooth, and improve the prediction accuracy of the Autoregressive Integrated Moving Average model (ARIMA). The Deep Belief Network (DBN) can effectively process a large number of complex data and deep mining the data features. While the empirical mode decomposition (EMD) can decompose the more stable data and amplify the details in the signal as much as possible. Meanwhile, the simulated annealing algorithm (SA) can avoid the network falling into a local optimal solution and improve the prediction accuracy. This paper uses a large number of photovoltaic power station data for experimental verification. The results show that this combined model has high accuracy and generalization ability.
Unsupervised Multi-view CNN for Salient View Selection of 3D Objects and Scenes

We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class. To validate its effectiveness, we design a multi-view CNN for the salient view selection of 3D objects, which quintessentially cannot be handled by supervised learning due to the difficulty of data collection. Our unsupervised multi-view CNN branches off two channels which encode the knowledge within each 2D view and the 3D object respectively and also exploits both intra-view and inter-view knowledge of the object. It ends with a new loss layer which formulates the view-object consistency by impelling the two channels to generate consistent classification outcomes. We experimentally demonstrate the superiority of our method over state-of-the-art methods and showcase that it can be used to select salient views of 3D scenes containing multiple objects.
Anthropometric Analysis of Face using Local Gaussian Distribution Fitting Applicable for Facial Surgery

Anthropometric Analysis of Face using Local Gaussian Distribution Fitting Applicable for Facial Surgery
Analytic Method for Decision Support of Blockchain Technology Supplier Selection in Industry 4.0 Era

Abstract The article presents the issues covering the modern methods of securing data in both manufacturing processes and companies within the concept of Industry 4.0. In this approach, research problems arose how to implement the right method of secure data sending in sales, manufacturing and distribution processes. It is a very important issue for manufacturing companies as well as how the process of sending electronic data should be safely conducted. While current researches concentrated on the method of blockchain secured electronic documents, there is almost no research concentrating on blockchain integrator selection criteria. The main purpose of this paper is to provide a decision assistance model based on multiple criteria decision analysis technique. Also, mutual relationships between parameters for the assessment of integrators are established. In the article, a Multi-Criteria Decision Analysis (MCDA) was used to assess these characteristics. The Decision Making Trial and Evaluation Laboratory (DEMATEL) technique was chosen for this assessment.
Verifiable Color Progressive Visual Cryptography with Cheating Detection

This paper proposes a progressive visual cryptography (PVC) scheme for color images without any pixel expansion based on the halftoning technique and primitive share. Firstly, a chromatic image is decomposed into three monochromatic images possessing channel red, blue and green. These three channels are transformed into binary images by halftone technique. Then progressive VCS(t, n) is applied to these channels, shares are produced depending upon n, these shares are progressive in nature and are known as primitive shares. Final share is generated by stacking one share from each channel. Hence, the primitive share is progressive, so the final shares are also progressive in nature. For reconstruction of secret shares are XORed, in which each channel of one share is XORed with the same channel in other share. To prevent attack from hackers and for cheating prevention, we are using Diagonal Parity for each possible diagonal and a weight called Gama weight. These are calculated for each share and are transmitted with the share. At the receiving side, these are again calculated and matched with the original parity and weight. This scheme provides progressive VCS to color image and an efficient method for cheating detection.
Video Action Recognition Based on Hybrid Convolutional Network

Aiming at the problem of unbalanced distribution of spatio-temporal information in video images, this paper proposes a 2D/3D hybrid convolutional network that introduces attention mechanism, which fully captures video space information and dynamic motion information, and better reveals motion features. With the help of the dual-stream convolutional network structure, we built 2D convolution and 3D convolution parallel neural networks. In the 2D convolutional neural network, the residual structure and the LSTM network model are used to focus on the spatial feature information of the video behavior. Secondly, the 3D convolutional neural network constructed by Inception structure is used to extract the spatiotemporal feature information of video behavior. On the basis of the two high-level semantics extracted, the attention mechanism is introduced to fuse the features. Finally, the obtained significant feature vector is used for video behavior recognition. Compared with other network models on the UCF101 and HMDB51 datasets, it can be seen from the results that the proposed 2D/3D hybrid convolutional network has good recognition performance and robustness.