Reviewerly/exHarmony huggingface dataset:
Classification of open mathematical problems and their role in academic achievement and motivation of students

Problems of developing subject education and science have become more acute during different social changes. The most problematic is the teaching of exact disciplines, including mathematical disciplines (mathematics, algebra, geometry), which form knowledge for working in high-tech industries. It is worth noting that the methodology of teaching mathematics has accumulated a large unsystematized portfolio of new techniques and methods. Thus, open problems aimed at developing students’ creativity are used more actively in the lessons. Tasks that require students to use non-standard approaches to the solution affect academic success. But a teacher of mathematics needs to have a classification of problems in the educational process to choose tasks of an open type correctly. In this regard, the article deals with substantiating the classification of open problems for teaching mathematical disciplines and developing methodological guidelines for lessons that help increase learning motivation and academic achievement of students. The leading research methods are the following: observation of teachers’ methodological work, talks with teachers, analysis of methodological developments, educational documentation and teachers’ surveys. Statistical processing of the results using Wilcoxon W test. In 2020-2021 the authors carried out an experiment with 358 students from 16 classes. Based on its results, the authors of the article have identified a classification of open problems for teaching mathematical disciplines; developed and implemented methodological guidelines for lessons of mathematics that contribute to developing students’ creativity in the academic progress. The authors evaluated the effectiveness of using the selected classification of open problems for teaching mathematical disciplines. They concluded about the increase of group motivation among students and academic performance in mathematical disciplines. The practical use of the classification of open problems allows the teacher to differentiate educational problems in mathematics, taking into account the relationship between their logical structure and the level of ability achieved by the student to solve problems in mathematics. Methodological guidelines developed by the authors can be used to help increase the learning motivation and academic achievement of students while preparing for mathematics lessons.
Quantum technologies: a course for teacher professional development.

Abstract We present an educational path for teacher professional development whose primary purpose is to enhance physics teachers’ knowledge and awareness of topics related to quantum computation and quantum information, and of their relevance for technological advancement. Besides their objective importance, also stressed by several authors and projects, the choice of topics not traditionally covered in the final year physics curricula also arises from the concrete possibility of developing a multidisciplinary path, able to represent under a unified perspective several subjects treated in secondary school physics and mathematics. The project is realized in the context of the Italian PLS (Plan for Science Degrees) and the education section of the Quantum Flagship. Due to the limitations related to the COVID-19 pandemic it was entirely delivered in the form of synchronous distance learning and was attended by around 30 teachers. Asynchronous discussion was performed using both generally available tools (Google drive, forms etc.) and a dedicated online forum set up on the servers of the University of Pavia. We discuss the structure of the educational path and the results of the first part of the course whose purpose was describing the transition from classical to quantum computation. In general, from both the written pre-questionnaire and the mid-course interviews, strong appreciation and fascination emerge for the cultural significance of the introduced topics and connections.
Password authenticated key exchange protocol for secure communication channel in modern honey network

Modern Honey Network (MHN) is regarded as the system collecting and integrating attacker's activity log data from various type of Honeypot Sensor (HS). MHN further applies a special protocol to communicate with Honeypot sensors, which is Hpfeeds. The application of Transport Layer Security (TLS) protocol to secure data transmission using Hpfeeds protocol is however deemed inefficient in the case of private system. In this study, the Password Authenticated Key Exchange (PAKE) is implemented for secure communication channel between HS and MHN on Hpfeeds protocol. The experiment result indicates that HS and MHN establish the identic symmetric key in the average time of 365.03 ms, with the assumption that prime number, prime divisor and random secret used for key establishment are predetermined. Meanwhile, the time required to generate the largest prime divisor exponentially increases along with the increasing prime bits number.
Anomaly detection in the context of long-term cloud resource usage planning

Abstract This paper describes a new approach to automatic long-term cloud resource usage planning with a novel hybrid anomaly detection mechanism. It analyzes existing anomaly detection solutions, possible improvements and the impact on the accuracy of resource usage planning. The proposed anomaly detection solution is an important part of the research, since it allows greater accuracy to be achieved in the long term. The proposed approach dynamically adjusts reservation plans in order to reduce the unnecessary load on resources and prevent the cloud from running out of them. The predictions are based on cloud analysis conducted using machine learning algorithms, which made it possible to reduce costs by about 50%. The solution was evaluated on real-life data from over 1700 virtual machines.
Direct Folding Method of Cylindrical Airbag and Its Application in Landing Buffer

The cylindrical airbag is widely used in the landing buffer field with a simple structure. It is a three-dimensional closed structure, so the folding model is difficult to establish. Because the modeling process of the existing methods is complex and these methods are difficult to use in the actual folding process, a new direct folding method was proposed in this paper. In the method, the two end faces of the cylindrical airbag are recessed inward, and the cylindrical surface is flattened into a sheetlike structure; then, its finite-element model is established. The folded cylindrical airbag was inflated, and the deployed shape was the same as that of the initial unfolded model after inflation. Moreover, the maximum errors of the equivalent cross-sectional diameter, maximum length, and volume of the inflated airbag were less than 4%, which shows that the method is reasonable and feasible. Based on this method, three finite-element models of cylindrical airbags in different folding states were established, and then the effects of folding mode, inflation rate, and ambient pressure on the deployment process of the folding airbag were studied. In the landing buffer analysis, this method was used to model the folded secondary airbag when establishing the finite-element model for the cushioning dynamic of the combined airbag. The simulation results are in good agreement with the drop test results, which also verifies the effectiveness of this folding method.
Constructions of projective linear codes by the intersection and difference of sets

Projective linear codes are a special class of linear codes whose dual codes have minimum distance at least 3. Projective linear codes with only a few weights are useful in authentication codes, secret sharing schemes, data storage systems and so on. In this paper, two constructions of q-ary linear codes are presented with defining sets given by the intersection and difference of two sets. These constructions produce several families of new projective two-weight or three-weight linear codes. As applications, our projective codes can be used to construct secret sharing schemes with interesting access structures, strongly regular graphs and association schemes with three classes.
Reversible data hiding scheme exploiting center folding with fuzzy weight strategy

Despite the fact that image interpolation is a well-established strategy in image processing, and many scholars have outlined numerous data hiding schemes based on it but current demand still has flaws. In this paper, a novel efficient reversible data hiding scheme has been developed after performing image interpolation by exploiting center folding with fuzzy weight strategy (FWS). The interpolated pixel values are obtained by considering the fuzzy weight of each pair of pixels in an selected image block. The fuzzy membership values of each input pixel pair has been regarded the range between minimum and maximum value of that block. The fuzzy output function feed into the input membership value and then the strength of the fuzzy rule has been calculated using Max–Min composite principle. After that, based on the strength of fuzzy rule, interpolated pixel value are computed from fuzzy output function through defuzzification process. In fact, fuzzy weight based interpolation algorithms produce virtual pixel, which is better than existing interpolation schemes. Then data hiding operation has been performed in each interpolated pixel through center folding strategy comparing with signal of secret data. If the signal of the secret data is different from the interpolated pixel, then the proposed scheme reverses the folded value to match the secret data for decreasing the image distortion between the interpolated image and the stego-image. At the receiver’s end, the secret data can be obtained based on the difference of the results of these interpolated values with center folding strategy (CFS). The proposed FWS achieved visual quality measured by PSNR 29.99 (dB) which is higher than existing image interpolations schemes. Furthermore, after embedding secret data through CFS we achieve PSNR0 33.40 (dB), PSNR1 33.45(dB) and PSNR2 48.012 (dB) for k=2 with embedding capacity 1.48 bpp which is more higher than state of the other methods. The experimental outcomes indicate that the proposed model is worthy to encode huge amount of secret bits with a enhanced visible quality, which can be anticipated by examining with existing ideas. The conveyed phenomenon emphasized some effective noble characteristics in the research area of hidden data communication for authentication, tampered detection without which today’s high-tech life stunts. This method gains exceptionally from many aspects of private, public or government sectors including education, economy, military and medical application.
Online learning control for path-aware global optimization with nonlinear mobile robots

Consider a robot with nonlinear dynamics that must quickly find a global optimum of an objective function defined over its operating area, e.g., a chemical concentration, physical measurement, quantity of material etc. The function is initially unknown and must be learned online from samples acquired in a single trajectory. Applying classical optimization methods in this scenario would be highly suboptimal, since they would place the next sample arbitrarily far, without taking into account robot motion constraints, and would not revise the path based on new information accumulated along it. To address these limitations, a novel algorithm called Path-Aware Optimistic Optimization (OOPA) is proposed. The decision of which robot action to apply is formulated as an optimal control problem in which the rewards are refinements of the upper bound on the objective, weighted by bound and objective values to focus the search around optima. OOPA is evaluated in extensive simulations where it is compared to path-unaware optimization baselines, and in a real experiment in which a ROBOTIS TurtleBot3 successfully searches for the lowest grayscale location on a 2D surface.
The Accursed Share of Non-fungibles

As described by Georges Bataille in his 1949 essays on General Economy, the Accursed Share refers to the excessive and non-recuperable portions of an economy that must be spent lavishly or luxuriously, or otherwise be destined to outrageous, destructive, and catastrophic expressions — most often resulting in war, or other ruinous and destructive acts [Baitaille and Hurley 1988]. How might we might consider Bataille's idea as expressed through the recent boom in Non-Fungible Tokens (NFTs) and contemporary Blockchain technology? Between 2020 and 2021, the global trading volume of NFTs increased ten-fold [Nadini et al. 2021], with a reported global market of 22 billion USD in 2021 [Milmo 2021]. Researchers Matthieu Nadini (et al.) note that digital art sales accounted for roughly 10 percent of all NFT transactions in 2021 [Nadini et al. 2021]. The volume and frequency of the booming NFT market cannot be ignored, but what does it suggest for the future of art practice and pedagogy, and how does it relate to the excesses suggested in Bataille's notion of general economy?
Exploring superadditivity of coherent information of noisy quantum channels through genetic algorithms

Machine learning techniques are increasingly being used in fundamental research to solve various challenging problems. Here we explore one such technique to address an important problem in quantum communication scenario. While transferring quantum information through a noisy quantum channel, the utility of the channel is characterized by its quantum capacity. Quantum channels, however, display an intriguing property called super-additivity of coherent information. This makes the calculation of quantum capacity a hard computational problem involving optimization over an exponentially increasing search space. In this work, we first utilize a neural network ansatz to represent quantum states and then apply an evolutionary optimization scheme to address this problem. We find regions in the three-parameter space of qubit Pauli channels where coherent information exhibits this super-additivity feature. We characterised the quantum codes that achieves high coherent information, finding several non-trivial quantum codes that outperforms the repetition codes for some Pauli channels. For some Pauli channels, these codes displays very high super-additivity of the order of 0.01, much higher than the observed values in other well studied quantum channels. We further compared the learning performance of the Neural Network ansatz with the raw ansatz to find that in the three-shot case, the neural network ansatz outperforms the raw representation in finding quantum codes of high coherent information. We also compared the learning performance of the evolutionary algorithm with a simple Particle Swarm Optimisation scheme and show empirical results indicating comparable performance, suggesting that the Neural Network ansatz coupled with the evolutionary scheme is indeed a promising approach to finding non-trivial quantum codes of high coherent information.
An effective context‐focused hierarchical mechanism for task‐oriented dialogue response generation

Abstract Task‐oriented dialogue system (TOD) is one kind of application of artificial intelligence (AI). The response generation module is a key component of TOD for replying to user's questions and concerns in sequential natural words. In the past few years, the works on response generation have attracted increasing research attention and have seen much progress. However, existing works ignore the fact that not each turn of dialogue history contributes to the dialogue response generation and give little consideration to the different weights of utterances in a dialogue history. In this article, we propose a hierarchical memory network mechanism with two steps to filter out unnecessary information of dialogue history. First, an utterance‐level memory network distributes various weights to each utterance (coarse‐grained). Second, a token‐level memory network assigns higher weights to keywords based on the former's output (fine‐grained). Furthermore, the output of the token‐level memory network will be employed to query the knowledge base (KB) to capture the dialogue‐related information. In the decoding stage, we take a gated‐mechanism to generate response word by word from dialogue history, vocabulary, or KB. Experiments show that the proposed model achieves superior results compared with state‐of‐the‐art models on several public datasets. Further analysis demonstrates the effectiveness of the proposed method and the robustness of the model in the case of an incomplete training set.
Construction of Interactive Higher Education Model Based on “VR + Internet”

With the popularization of Internet and VR technology in various fields, people’s lifestyle has gradually changed, and the field of educational engineering is constantly innovating and developing. Among many educational technologies, VR virtual reality technology is particularly eye-catching. The rapid development of VR technology has created new opportunities for all fields. Realizing VR in the industry can promote the development and progress of the industry itself. Network education is the product of the combination of information technology and computer technology. The combination of VR technology and Internet will inevitably promote the further development of interactive college education mode. China is facing the crisis of network education reform and development, but the combination of internet and education is the objective need of internet development and education development. The interactive mode of higher education not only promotes the innovation of higher education mode but also optimizes and innovates the structure of interactive education field. The combination of VR technology and Internet can make students better integrate into the teaching situation, improve learning efficiency, and effectively achieve the educational purpose. Therefore, it can be predicted that the interactive college education mode is an inevitable trend in the development of interactive education. Based on this, this paper first makes a brief analysis of VR technology and the Internet, then examines the significance of realizing the combination of the two, and finally puts forward the strategies and methods to realize the combination of interactive education and VR technology.
M-neighbor approximation in one-qubit state transfer along zigzag and alternating spin-1/2 chains

We consider the $M$-neighbor approximation in the problem of one-qubit pure state transfer along the $N$-node zigzag and alternating spin chains governed by the $XXZ$-Hamiltonian with the dipole-dipole interaction. We show that always $M>1$, i.e., the nearest neighbor approximation is not applicable to such interaction. Moreover, only all-node interaction ($M=N-1$) properly describes the dynamics in the alternating chain. We reveal the region in the parameter space characterizing the chain geometry and orientation which provide the high-probability state-transfer. The optimal state-transfer probability and appropriate time instant for the zigzag and alternating chains are compared.
An experimental comparison of the widely used pre‐trained deep neural networks for image classification tasks towards revealing the promise of transfer‐learning

Summary The easiest way to propose a solution based on deep neural networks is using the pre‐trained models through the transfer‐learning technique. Deep learning platforms provide various pre‐trained deep neural networks that can be easily applied for image classification tasks. So, “ Which pre‐trained model provides the best performance for image classification tasks? ” is a question that instinctively comes to mind and should be shed light on by the research community. To this end, we propose an experimental comparison of the six popular pre‐trained deep neural networks, namely, (i) VGG19 , (ii) ResNet50 , (iii) DenseNet201 , (iv) MobileNetV2 , (v) InceptionV3 , and (vi) Xception by employing them through the transfer‐learning technique. Then, the proposed benchmark models were both trained and evaluated under the same configurations on two gold‐standard datasets, namely, (i) CIFAR‐10 and (ii) Stanford Dogs to benchmark them. Three evaluation metrics were employed to measure performance differences between the employed pre‐trained models as follows: (i) Accuracy , (ii) training duration , and (iii) inference time . The key findings that were obtained through the conducted a wide variety of experiments were discussed.
Evaluation of English Proficiency Based on Big Data Clustering Algorithm

As one of the common languages in the world, English is playing a more and more important role in trade exchanges, cultural exchanges, and other transnational cooperation. In order to better integrate into the world and communicate with the world, China began to vigorously expand English learning as early as the late 1980s. However, with the development trend of economic globalization and cultural diversity, the traditional English ability evaluation methods are not enough to comprehensively measure individual English ability. Therefore, based on this background, this paper introduces the clustering algorithm into English ability evaluation, improves the traditional clustering algorithm represented by <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means on the basis of predecessors, and captures the evaluation data through neural network algorithm combined with the background of modern big data framework. The results show that the fuzzy logic feature mapping clustering algorithm (FLFM) proposed in this paper performs better than the traditional evaluation algorithm, and the evaluation effectiveness and comprehensiveness are improved by about 10 pt.
Machine Learning Methods for Quantifying Uncertainty in Prospectivity Mapping of Magmatic-Hydrothermal Gold Deposits: A Case Study from Juruena Mineral Province, Northern Mato Grosso, Brazil

Mineral prospectivity mapping (MPM), like other geoscience fields, is subject to a variety of uncertainties. When data about unfavorable sites to find deposits (i.e., drill intersections to barren rocks) is lacking in MPM using machine learning (ML) methods, the synthetic generation of negative datasets is required. As a result, techniques for selecting point locations to represent negative examples must be employed. Several approaches have been proposed in the past; however, one can never be certain that the points chosen are true negatives or, at the very least, optimal for training. As a consequence, methodologies that account for the uncertainty of the generation of negative datasets in MPM are needed. In this paper, we compare two criteria for selecting negative examples and quantify the uncertainty associated with this process by generating 400 potential maps for each of the three ML methods utilized (200 maps for each criterion), which include random forest (RF), support vector machine (SVM), and k-nearest neighbors (KNC). The results showed that applying a geological constraint to the creation of negative datasets reduced prediction uncertainty and improved overall model performance but produced larger areas of very high probability (i.e., &gt;0.9) when compared to using only the spatial distribution of known deposits and occurrences as a constraint. SHAP values were used to find approximations for the importance of features in nonlinear methods, and kernel density estimations were used to examine how they varied depending on the negative dataset used to train the ML models. Prospectivity models for magmatic-hydrothermal gold deposits were generated using data from the shuttle radar terrain mission, gamma-ray, magnetic lineaments, and proximity to dykes. The Juruena Mineral Province, situated in Northern Mato Grosso, Brazil, represented the case study for this work.