Reviewerly/exHarmony huggingface dataset:
Smooth input preparation for quantum and quantum-inspired machine learning

Abstract Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.
Digital Skills of Teachers

The aim of the work is to analyze the digital skills of teachers of Russian universities a year after the COVID-19 pandemic. The latest research on the digitalization of education, analysis of the digital competencies of teachers and tools to evaluate the digital skills of educators has been conducted. The results of a study conducted on a sample of Russian teachers are described. It has been revealed that teachers have an average level of digital literacy, and most educators are integrators and experts in the use of technology in the educational process. They are able to assess educational resources, create digital resources and share digital materials, cope with the problem of changing existing digital courses, etc. The findings are of interest both to the university’s professional development system, which develops the digital competence of its teachers, and for teachers who wish to improve their level of digital literacy through self-education.
Convergence of employers’ and students’ expectations in the educational environment of the agricultural university

In the digital age, a modern university is becoming a driver for the development of complex multi-agent ecosystems and a supplier of a new type of human resources, characterized not only by intellectual potential and high qualifications but also by such qualities in demand in the labor market as creativity, critical thinking, the need for continuous learning and “agency”, which is understood as the ability to self-organize and proactivity [1]. The education system and universities themselves face challenges that force us to turn to the once-actual ideas of integration and convergence, which can fulfill the increased need to achieve synergy effects and multiplier indicators that contribute to achieving high ratings in a short time. It is convergent processes that lie at the heart of the developing communicative space of the university today, whose activities are based on the intersection of the interests and expectations of various actors, which are implemented on multi-agent platforms. At the same time, the opportunities that are opening up due to the rapid introduction of digital technologies require restructuring the space of interactions between subjects towards the formation of flexible forms, network models, alliances, virtual corporations, connecting clusters built on the principles of active cooperation, exchange of best practices and developments.
OctoPath: An OcTree-Based Self-Supervised Learning Approach to Local Trajectory Planning for Mobile Robots

Autonomous mobile robots are usually faced with challenging situations when driving in complex environments. Namely, they have to recognize the static and dynamic obstacles, plan the driving path and execute their motion. For addressing the issue of perception and path planning, in this paper, we introduce OctoPath, which is an encoder-decoder deep neural network, trained in a self-supervised manner to predict the local optimal trajectory for the ego-vehicle. Using the discretization provided by a 3D octree environment model, our approach reformulates trajectory prediction as a classification problem with a configurable resolution. During training, OctoPath minimizes the error between the predicted and the manually driven trajectories in a given training dataset. This allows us to avoid the pitfall of regression-based trajectory estimation, in which there is an infinite state space for the output trajectory points. Environment sensing is performed using a 40-channel mechanical LiDAR sensor, fused with an inertial measurement unit and wheels odometry for state estimation. The experiments are performed both in simulation and real-life, using our own developed GridSim simulator and RovisLab’s Autonomous Mobile Test Unit platform. We evaluate the predictions of OctoPath in different driving scenarios, both indoor and outdoor, while benchmarking our system against a baseline hybrid A-Star algorithm and a regression-based supervised learning method, as well as against a CNN learning-based optimal path planning method.
Psychological Factors Influencing on the Motivation to Study of Students of TEI

In the course of the investigation, it has been found that the influence of psychological factors is quite effective in the case of motivating students to study. It has been proven that increasing the motivation of students to study is possible through: a common learning environment in the students’ collective; students’ participation in discussions, conferences, competitions; work in groups; application of innovative technological methods of studying educational material.
Digital, analog, electronic or virtual: Which is right?

The library science terminology related to the new information technologies has become unstable with the emergence of the term “digital” as an alternative to the term “electronic”. The author expresses high opinion of the analysis of these terms, accomplished by Ksenia Yu. Volkova and Victor V. Zverevich in their article “Digital or electronic” (Nauch. and tech. b-ki. - 2020. - № 12. -P. 159-172), and as a follow-up, digs into the semantic difference of the terms content. In the professional literature, the discussion of modern terminology began with the term "virtual" in the 1990s. It was at the same time that the short life for the term was predicted, and experience speaks to the truth of the prediction. Electronic and digital document types exist in the technodocument cluster and - further - only of its technotronic subtype. The technotronic document is a document which creation and reproduction require modern technical instruments and technologies. Electronic document is the most modern type of technotronic document for the moment. The digital - electronic contraposing, which is widely used in the modern world library literature, is incorrect. In fact, the digital opposes the analog. Electronic is a general term applied both to digital and analog, as well as to virtual. The terms electronic and non-electronic make the dichotomous pair of concepts. The author foresees long relevance of the concept of electronic library science.
Scale Equivariant Neural Networks with Morphological Scale-Spaces

The translation equivariance of convolutions can make convolutional neural networks translation equivariant or invariant. Equivariance to other transformations (e.g. rotations, affine transformations, scalings) may also be desirable as soon as we know a priori that transformed versions of the same objects appear in the data. The semigroup cross-correlation, which is a linear operator equivariant to semigroup actions, was recently proposed and applied in conjunction with the Gaussian scale-space to create architectures which are equivariant to discrete scalings. In this paper, a generalization using a broad class of liftings, including morphological scale-spaces, is proposed. The architectures obtained from different scale-spaces are tested and compared in supervised classification and semantic segmentation tasks where objects in test images appear at different scales compared to training images. In both classification and segmentation tasks, the scale-equivariant architectures improve dramatically the generalization to unseen scales compared to a convolutional baseline. Besides, in our experiments morphological scale-spaces outperformed the Gaussian scale-space in geometrical tasks.
Testing coverage criteria for optimized deep belief network with search and rescue

Abstract A new data-driven programming model is defined by the deep learning (DL) that makes the internal structure of a created neuron system over a fixed of training data. DL testing structure only depends on the data labeling and manual group. Nowadays, a lot of coverage criteria have been developed, but these criteria basically count the neurons' quantity whose activation during the implementation of a DL structure fulfilled certain properties. Also, existing criteria are not adequately fine-grained to capture delicate behaviors. This paper develops an optimized deep belief network (DBN) with a search and rescue (SAR) algorithm for testing coverage criteria. For an optimal selection of DBN structure, the SAR algorithm is introduced. The main objective is to test the DL structure using different criteria to enhance the coverage accuracy. The different coverage criteria such as KMNC, NBC, SNAC, TKNC, and TKNP are used for the testing of DBN. Using the generated test inputs, the criteria is validated and the developed criteria are capable to capture undesired behaviors in the DBN structure. The developed approach is implemented by Python platform using three standard datasets like MNIST, CIFAR-10, and ImageNet. For analysis, the developed approach is compared with the three LeNet models like LeNet-1, LeNet-4 and LeNet-5 for the MNIST dataset, the VGG-16, and ResNet-20 models for the CIFAR-10 dataset, and the VGG-19 and ResNet-50 models for the ImageNet dataset. These models are tested on the four adversarial test input generation approaches like BIM, JSMA, FGSM, and CW, and one DL testing method like DeepGauge to validate the efficiency of the suggested approach. The simulation results proved that the proposed approach obtained high coverage accuracy for each criterion on four adversarial test inputs and one DL testing method as compared to other models.
Prediction of Multiple sclerosis disease using machine learning classifiers: a comparative study.

Hamedan Province is one of Iran's high-risk regions for Multiple Sclerosis (MS). Early diagnosis of MS based on an accurate system can control the disease. The aim of this study was to compare the performance of four machine learning techniques with traditional methods for predicting MS patients.The study used information regarding 200 patients through a case-control study conducted in Hamadan, Western Iran, from 2013 to 2015. The performance of six classifiers was used to compare their performance in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), negative likelihood ratio (LR-) and total accuracy.Random Forest (RF) model illustrated better performance among other models in both scenarios. It had greater specificity (0.67), PPV (0.68) and total accuracy (0.68). The most influential diagnostic factors for MS were age, birth season and gender.Our findings showed that despite all the six methods performed almost similarly, the RF model performed slightly better in terms of different criteria in prediction accuracy. Accordingly, this approach is an effective classifier for predicting MS in the early stage and control the disease.
Quantum Embedding Search for Quantum Machine Learning

This paper introduces a novel quantum embedding search algorithm (QES, pronounced as "quest"), enabling search for optimal quantum embedding design for a specific dataset of interest. First, we establish the connection between the structures of quantum embedding and the representations of directed multi-graphs, enabling a well-defined search space. Second, we instigate the entanglement level to reduce the cardinality of the search space to a feasible size for practical implementations. Finally, we mitigate the cost of evaluating the true loss function by using surrogate models via sequential model-based optimization. We demonstrate the feasibility of our proposed approach on synthesis and Iris datasets, which empirically shows that found quantum embedding architecture by QES outperforms manual designs whereas achieving comparable performance to classical machine learning models.
Impoliteness of Directive Speech Acts in Online Indonesian Language Learning

This study aims to describe the impoliteness of directive speech acts in online Indonesian language learning. The data collection technique in this study used the observation, note, and record technique. The object of this research was the analysis of directive speech act impoliteness. The data analysis technique used in this study was a data triangulation model. The study results indicate an impoliteness of directive speech acts on Indonesian language learning conducted by the teacher. The teacher unintentionally performed impoliteness on the directive speech acts. The first data found that the teacher asked all the students to pay attention impolitely. The second data showed that the teacher as a speaker prohibits students from taking attendance. The third data showed that the teacher used the impolite directive speech acts when saying the utter "unnecessary" and "you pay less attention" to the students who forgot to attend the class. The data (3a) above includes the impoliteness of the directive speech act of the requesting because it does not contain politeness elements that can smooth speech. Data (4a) The teacher asks students who are not members to leave the WhatsApp group, but the teacher does not use soft sentences. Data (5a) stated that the teacher instructs the students to cut the paper using a cutter and make lines on it . Next, the data (5b) stated the teacher asks students to look at the learning material using impoliteness directive speech acts. Data (5c) stated that the teacher instructs students not to forget to fill the attendance. Data (6a) stated the teacher asks students to join the google classroom but does not use polite sentences. The data includes the directive speech act of the requesting marked with the word beg. Data (7a) Teachers require students to have sufficient quotas when participating in learning Indonesian online. Keywords: impoliteness, directive speech acts, Online learning
Over The Air Performance of Deep Learning for Modulation Classification across Channel Conditions

Deep learning (DL) models used for modulation classification are mostly trained on simulated data. Their performance drops significantly on real test data, due to disparity in probability distributions between simulated and real data. The eventual goal is building a DL model classifying modulation type accurately on real data. This work empirically studies the performance impact due to disparity in probability distributions between training and test data. We borrow best performing deep learning models from literature for our analysis. Models are tested on data belonging to channel conditions they were trained on and otherwise. Software defined radios (SDR) collect training and test data under channel conditions of additive white Gaussian noise, line-of-sight (LOS) and non-line-of-sight (NLOS). Convolutional neural network (CNN) and Residual neural network (ResNet) architectures are used. Test accuracies of the models are compared across model architectures, channel conditions, modulation types and SNR. Performance results of DL models on real data, are presented for wide set of scenarios. Dataset is available for download and can be used for evaluating deep learning models.
Determining the Effect of Science Teaching Using Mobile Augmented Reality Application on the Secondary School Students’ Attitudes of toward Science and Technology and Academic Achievement

In this research, the effects of science education using mobile augmented reality (MAR) application on students’ attitudes toward science, technology, and their academic achievement were determined against two socioeconomic levels. The parallel pattern mixed method was used in the study. The research was carried out with 143 sixth-grade students from two schools with different socioeconomic levels in Turkey. The MAR application was used with the experimental groups. Quantitative data were collected through the “Science and Technology Attitude Scale” and “Systems Achievement Test.” According to the ANCOVA results, it was observed that the MAR application used in the processing of the lessons was effective in improving the academic achievement levels of the students but not in attitudes toward science and technology. Results showed that the socioeconomic situation played an active role on the academic achievement of students but did not play an effective role on science and technology attitudes. The results of this research are important in determining the differences between students at different socioeconomic levels experiencing technology-supported systems.
Exploring the representational power of graph autoencoder

While representation learning has yielded a great success on many graph learning tasks, there is little understanding behind the structures that are being captured by these embeddings. For example, we wonder if the topological features, such as the Triangle Count, the Degree of the node and other centrality measures are concretely encoded in the embeddings. Furthermore, we ask if the presence of these structures in the embeddings is necessary for a better performance on the downstream tasks, such as clustering and classification. To address these questions, we conduct an extensive empirical study over three classes of unsupervised graph embedding models and seven different variants of Graph Autoencoders.Our results show that five topological features: the Degree, the Local Clustering Score, the Betweenness Centrality, the Eigenvector Centrality, and Triangle Count are concretely preserved in the first layer of the graph autoencoder that employs the SUM aggregation rule, under the condition that the model preserves the second-order proximity. We supplement further evidence for the presence of these features by revealing a hierarchy in the distribution of the topological features in the embeddings of the aforementioned model. We also show that a model with such properties can outperform other models on certain downstream tasks, especially when the preserved features are relevant to the task at hand. Finally, we evaluate the suitability of our findings through a test case study related to social influence prediction.
Research on Resource Personalization Recommendation Algorithm and Model Based on Computer Deep Learning

Abstract In the big data environment, the network learning environment has the problems of “information overload” and “information trek”. In the online learning process, if we can obtain an intelligent, targeted, and personalized learning resource recommendation service, the learning efficiency and learning experience will be greatly improved. Personalized learning resource recommendation technology has gradually become an important research topic in the field of education and intelligent information processing. This paper mainly combines deep learning and recommendation algorithms to design a framework to improve the accuracy of recommendation results.
Private Adaptive Gradient Methods for Convex Optimization

We study adaptive methods for differentially private convex optimization, proposing and analyzing differentially private variants of a Stochastic Gradient Descent (SGD) algorithm with adaptive stepsizes, as well as the AdaGrad algorithm. We provide upper bounds on the regret of both algorithms and show that the bounds are (worst-case) optimal. As a consequence of our development, we show that our private versions of AdaGrad outperform adaptive SGD, which in turn outperforms traditional SGD in scenarios with non-isotropic gradients where (non-private) Adagrad provably outperforms SGD. The major challenge is that the isotropic noise typically added for privacy dominates the signal in gradient geometry for high-dimensional problems; approaches to this that effectively optimize over lower-dimensional subspaces simply ignore the actual problems that varying gradient geometries introduce. In contrast, we study non-isotropic clipping and noise addition, developing a principled theoretical approach; the consequent procedures also enjoy significantly stronger empirical performance than prior approaches.
Improving the Accuracy of Early Software Size Estimation Using Analysis-to-Design Adjustment Factors (ADAFs)

Early software size estimation is a challenging task since limited information is available at the time of project inception. Additional information, however, is gradually added as development progresses. The goal of this research is to quantitatively capture the impact on early software size estimation of this additional information introduced especially when transitioning from the analysis phase to the design phase by comparing the analysis class diagram (ACD) and the design class diagram (DCD). We introduce a new class of metrics called analysis-to-design adjustment factors (ADAFs) to accomplish this goal. ADAFs are calculated for four different class diagram metrics - number of classes (NOC), number of attributes (NOA), number of methods (NOM), and number of relationships (NOR) - used in different class diagram-based software size estimation models. We use practical, theoretical, and empirical validation methods to evaluate the applicability of these ADAFs. To assess the utility of these ADAFs in early software size estimation, we compare the accuracy of existing early software size estimation models before and after the application of ADAFs. Results indicate a marked improvement in the accuracy of these models after the application of ADAFs. Furthermore, regression-based models employing problem domain metrics have also been built to predict these ADAFs. All of these models are statistically significant (p-values <; 0.05) with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> values between 0.42 and 0.88.
Knowledge Router: Learning Disentangled Representations for Knowledge Graphs

The design of expressive representations of entities and relations in a knowledge graph is an important endeavor.While many of the existing approaches have primarily focused on learning from relational patterns and structural information, the intrinsic complexity of KG entities has been more or less overlooked.More concretely, we hypothesize KG entities may be more complex than we think, i.e., an entity may wear many hats and relational triplets may form due to more than a single reason.To this end, this paper proposes to learn disentangled representations of KG entities -a new method that disentangles the inner latent properties of KG entities.Our disentangled process operates at the graph level and a neighborhood mechanism is leveraged to disentangle the hidden properties of each entity.This disentangled representation learning approach is model agnostic and compatible with canonical KG embedding approaches.We conduct extensive experiments on several benchmark datasets, equipping a variety of models (DistMult, SimplE, and QuatE) with our proposed disentangling mechanism.Experimental results demonstrate that our proposed approach substantially improves performance on key metrics.