Reviewerly/exHarmony huggingface dataset:
Using Domain Knowledge to Assist Process Scenario Discoveries

Trace clustering techniques are often used to assist in discovering different process scenarios. Most existing trace clustering methods aim to partition the event log into different subsets of logs where event traces in the same subset most likely belong to the same scenario. Typically, the partitioning is based on certain patterns, the similarity between the traces, or done by discovering a process model for each cluster of traces. However, most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. The domain expert may have certain domain expertise which should be exploited to create better process scenario models. This paper presents a density-based trace clustering technique based on expert knowledge to assist process scenario discovery results. A real wastewater treatment process provided by a domain expert is used as a case study to investigate the effectiveness and validity of the approach. We also use five real-life event logs to compare the performance of the process approach for process scenario discoveries with the commonly used k-means clustering approach. To measure the validity of our results, we take the weighted average fitness, the weighted average precision, and the F1 score. The experiment data show that (1) the proposed approach is able to discover the process scenarios from event logs by incorporating domain knowledge and (2) the process models obtained with the proposed approach have higher fitness, precision, and F1 scores than the models obtained by the commonly used k-means clustering approaches.
Quantum Key Recovery Attacks on 3-Round Feistel-2 Structure Without Quantum Encryption Oracles

The Feistel-2 (a.k.a, Feistel-KF) structure is a variant of the Feistel structure such that the i-th round function is given by $$\mathsf {F}_i(k_i \oplus x)$$ , where $$\mathsf {F}_i$$ is a public random function and its input/output length is n/2 bits. Isobe and Shibutani showed a meet-in-the-middle attack in the classical setting with $$(D,T)=(O(1),O(2^{n/2}))$$ on the 3-round Feistel-2 structure where D and T are the numbers of online/offline queries, respectively. In their attack, since two round keys are recovered simultaneously, a naive application of Grover’s algorithm for two keys needs $$T = O(2^{n/2})$$ in the quantum setting. In this paper, we introduce a new known plaintext attack and chosen plaintext attack on the 3-round Feistel-2 structure in the quantum setting using Grover’s algorithm by recovering the round key one by one in $$(D,T)=(O(1),O(2^{n/4}))$$ . Our attack does not need any quantum query to the encryption oracle (i.e., working in the Q1 model).
Real Time Safe Social Distance and Face Mask Detection using YOLO Algorithm and Deep Learning

The pandemic hit the world, and governments all around the world adopted drastic but necessary steps to stop it from spreading may be COVID or other versions. It’s difficult to ensure that individuals follow these important social distance principles in large institutions. An automated system is necessary to enable easy tracking of such offenders. As a result, this system was designed to identify specific infractions in real-time. The proposed system’s initial use is to recognize faces of people to decide whether they are wearing an approved mask or not. The other use is to evaluate whether the social distance is maintained between two people in the most efficient manner that is very accurate, and easy way possible, requiring the least amount of effort from supervisory authorities.
A value meta-model for crossover services modeling

As a new service model, crossover services have attracted wide attention from the industry and academia, and have the characteristics of diversified stakeholders, heterogeneous services and processes, which brings big challenges to the requirements modeling and analysis of crossover scenarios. Most of the traditional modeling methods focus on the functional and non-functional requirements, but pay less attention to the value propositions of stakeholders and value attributes of services, which leads to the failure of some crossover services. In this paper, a value meta-model is proposed to support the value modeling of crossover services. To evaluate the proposed method, a case study is presented to illustrate the usage of the proposed approach, and a controlled experiment is conducted to evaluate the usability of the proposed method. The experimental results demonstrate the effectiveness and usefulness of the proposed method.
Mapping User-Submitted Short Text Questions to Subjects of Study: A Multinomial Classification Approach

Text classification is the process of assigning labels to text documents for categorizing them. Accurate classification of user-submitted questions is of great use for any e-learning environment where the students are constantly submitting new questions that need to be presented to their appropriate subject experts for resolution. The user-submitted questions are typically short text documents, with a vastly disproportionate number of questions in different subjects. To resolve this, generalized linear model, distributed random forest, extremely randomized trees, gradient boosting model, extreme gradient boosting (XGBoost) and deep learning (ANN) models were trained and compared on a dataset of short text questions, while utilizing TF-IDF feature extraction and grid search for hyperparameter tuning. It was found out that an appropriately tuned XGBoost model gives the best performance out of these. The customized XGBoost model was then combined with a mechanism to handle imbalanced classes to propose a model that demonstrates further improved performance on the heavily imbalanced dataset.
Network Traffic Prediction Based on Double-Synchronized Periodic LSTM

In the prediction of time series, the data scale often changes rapidly or a data series shows periodic mutation trend characteristic. A general prediction algorithm cannot obtain the periodic change information of time series in advance, resulting in a limited prediction effect. Considering this factor, the structural improvement of periodic features in a general prediction algorithm can be more suitable for some real scenarios. To solve this problem, this paper proposes a network traffic prediction model based on double-synchronized periodic LSTM (DSP-LSTM), which can comprehensively consider the good fitting ability of linear model to periodic feature data and the nonlinear prediction characteristic of neural network. The experimental results show that the improved DSP-LSTM model can show a better fitting effect when time series data have periodic trend characteristics. Compared with general prediction algorithm, the DSP-LSTM model can effectively reduce the prediction error of periodic time series, improve the prediction accuracy, and adapt well to the changes of different time series patterns.
Unsupervised Markdown Feature-Aware Keywords Extraction Towards Technology Blogs

A vast amount of blogs are generated from online technology communities every day. Most of them are in Markdown format. The increase of Markdown documents has brought opportunities and challenges to many natural language processing tasks. Extracting keywords from technology blogs is of great value for discovering, retrieving, and sharing knowl-edge about technical blogs. The mainstream keyword extraction algorithms remain to use statistical char-acteristics of words to determine the keywords of a document, seldom considering the structure char-acteristics of the document that potentially express the semantic information. We argue that Markdown markup features as well as the textual content of the document are both concerned with the keywords extraction. In this paper, we propose a novel un-supervised Markdown markup features aware key-words extraction algorithm for technology blogs. The algorithm integrates Markdown markup syntax in-formation with a blog text representation. Through experiments against TF-IDF, TextRank, and PositionRank algorithms on a real Markdown document dataset, our algorithm achieves higher performance with a substantial improvement when the number of keywords extracted is greater than 3.
Augmented Reality Applications for Urban Cultural Heritage Sites: An Overview

Augmented Reality applications have become the newest technology used in the Cultural Heritage domain. These applications can be used in education and tourism. Various methods and software tools provide the means of designing such applications. This study provides an overview of the most recent Augmented Reality projects in Cultural Heritage sites in urban environments, comparing tracking methods, devices, themes, and settings used in each project. The most frequently used tracking method is camera-based, with handheld devices being almost entirely preferred in such projects. There is an even distribution of themes, while outdoor scenarios are the preferred setting.
LASSO Based Extreme Learning Machine for Spectral Multivariate Calibration of Complex Samples

Extreme learning machine (ELM) has received increasing attention in multivariate calibration of complex samples due to its advantages of fast learning speed and good generalization ability. However, irrelevant variables in spectral matrix to target can interfere the quality of ELM modeling. Therefore, variable selection is required before multivariate calibration. In this study, least absolute shrinkage and selection operator (LASSO) combined with ELM (LASSO-ELM) is used for spectral quantitative analysis of complex samples. In the method, LASSO is firstly used to selected variables by shrinking regression coefficients of unselected variables to zero. The optimal model position s of LASSO is determined by Sp criterion. Then ELM model is built between the selected variables and analyzed target with the optimal activation function and hidden node number determined by the ratio of mean to standard deviation of correlation coefficients (MSR). Near infrared (NIR) spectra of tobacco lamina and ultraviolet (UV) spectra of fuel oil samples are used to evaluate the prediction performance of LASSO-ELM. Results show that only with tens of variables, LASSO-ELM achieves the lowest root mean square error of prediction (RMSEP) and highest correlation coefficient (R) compared with full-spectrum partial least squares (PLS) and ELM. Thus, LASSO-ELM is an effictive variable selection and multivariate calibration method for quanatitive analysis of complex samples.
Power-aware pruning for ultrafast, energy-efficient, and accurate optical neural network design

With the rapid progress of the integrated nanophotonics technology, the optical neural network (ONN) architecture has been widely investigated. Although the ONN inference is fast, conventional densely connected network structures consume large amounts of power in laser sources. We propose a novel ONN design method that finds an ultrafast, energy-efficient, and accurate ONN structure. The key idea is power-aware edge pruning that derives the near-optimal numbers of edges in the entire network. Optoelectronic circuit simulation demonstrates the correct functional behavior of the ONN. Furthermore, experimental evaluations using tensor-flow show the proposed methods achieved 98.28% power reduction without significant loss of accuracy.
Cognitive Architecture for Naval Mission Management

Information analysis related to a merchant or Navy ship and its environment is required in order to make the appropriate decisions during naval missions. However, human capabilities are no longer sufficient to reliably and rapidly apply methodologies at their disposal in an uncertain and time-constrained environment. Decision-makers face with uncertain situations where they lack time and resources. To deal with this situation, the design and the development of a decision support system would make it possible, thanks to the combination of cognitive sciences and the computing power of modern information technology resources, to reduce the time required (1) to analyse the situation and (2) to select an appropriate action for the situation. This work is based on a decision-making method from Natural Decision Making called Recognition Primed Decision, used for emergency decision-making. The proposal is therefore to formalize a decision support system and more specifically a cognitive architecture based on Recognition Primed Decision associated to the adaptive capacities of the deep reinforcement learning, and capable of reacting in a dynamic environment with novel situations.
Aprender a enseñar en una sociedad conectada Learning to teach in a connected society

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
Multi-observed Multi-factor Authentication: A Multi-factor Authentication Using Single Credential

Nowadays, it is no longer uncommon for a PC to be infected with malware. User authentication based on only one legitimate credential (such as a password) may be insufficient for judging whether a user is legitimate. A typical solution to this problem is two-factor authentication, which is a method of authentication based on the presentation of two factors by the user. Generally, the first factor is input into a PC and the second into a smartphone. However, this reduces usability by forcing the user to present multiple factors at each authentication. Therefore, in this study, a method called multi-observed multi-factor authentication is proposed, whereby multi-factor authentication is performed to confirm that a human has input a legitimate credential by observing the user’s input of a single legitimate credential at multiple points.
Preface to the second edition

От авторов ко второму изданию - Institutional repository of Federal State Autonomous Educational Institution of Higher Professional Education Ural Federal University named after the first President of Russia B.N.Yeltsin
Evaluation of neural network algorithms for atmospheric turbulence mitigation

A variety of neural networks architectures are being studied to tackle blur in images and videos caused by a non-steady camera and objects being captured. In this paper, we present an overview of these existing networks and perform experiments to remove the blur caused by atmospheric turbulence. Our experiments aim to examine the reusability of existing networks and identify desirable aspects of the architecture in a system that is geared specifically towards atmospheric turbulence mitigation. We compare five different architectures, including a network trained in an end-to-end fashion, thereby removing the need for a stabilization step.
Optical Neural Network with Reduced Phase Shifters Using Multi-Plane Light Conversion

We investigate the effect of reducing the number of phase-shifting stages in optical neural networks. High data-classification accuracy over 95% is obtained with only 1/10 phase shifters when the multi-plane light-conversion architecture is employed.
Clusters of Repetition Roots Forming Prefix Chains

We investigate lower bounds on the size of clusters (sets of starting positions of occurrences) of common prefixes shared by repetition roots. Such lower bounds in terms of the constituent roots in the sets provide upper bounds on the number of distinct repetitions. In the case of distinct square roots which are totally ordered by the prefix relation it has been shown that there must be more occurrences of the common prefix than the number of roots. Here we develop the theory further by presenting the tools to extend the bounds to exponents higher than 2 and we show that they are optimal in the sense that any sequence of cluster sizes satisfying the lower bounds can be realized. We also take the next step towards the bounds on arbitrary (only partially prefix-ordered) sets of roots by proving a lower bound on unbordered prefixes shared by two overlapping prefix chains of roots.
Stochastic Markov $k$-Tree Network Sparsification Learning for Image Classification

Classification in computer science is for the purpose of designing and implementing an algorithmic classifier to identify the target category given by an observed data, which has played a key role within the recent decades. The author, in this paper, proposed a novel image classification learning method associated with machine learning by introducing network sparsification modelling algorithm based on stochastic Markov k-tree graph learning. The proposed Markov k-tree network sparsification learning demonstrated its overall state-of-art performance, as our testing and analysis proved, on multiple datasets which are generally well-used for the image classification application.
A novel design for a gated recurrent network with attentional memories

Recurrent neural networks (RNN) are used for pro-cessing the temporal dynamics of complex sequential information. Existing recurrent neural networks, such as Long Short-Term Memory (LSTM) and its variant, Gated Recurrent Unit (GRU), are used to control current and historical information. This study proposes a novel design for a gated recurrent neural network with an attention mechanism, named the Attentional Memory Unit (AMU), which gives the recurrent neural network an attention capability. This design uses a gated mechanism and a generalized attention mechanism so that it can be adaptively adjusted in the spatial and temporal sense to allow the application of the attention mechanism for different scenarios. The experimental results verify that the proposed AMU significantly boosts the power of RNNs.
Programming multi-mode quantum circuits in complex scattering media

We demonstrate the manipulation of a five-dimensional spatially entangled two-photon state using reprogrammable multi-mode optical circuits implemented within a multi-mode fiber.
The Target Recognition Algorithm based on the Coordinate Attention Mechanism and the Mutual-channel Loss

To order to meet the target detection and recognition needs of the helicopter / UA V cooperative situation awareness system, the target recognition algorithm based on the coordinate attention mechanism and mutual-channel Loss of UA V is proposed. Our method uses deep separable convolution and a double threshold attention mechanism to build a lightweight deep convolution neural network with strong feature extraction ability and uses a loss function based on feature channel information to drive the network model to obtain convolution features with strong discrimination. The experimental results show that the proposed algorithm can effectively obtain higher UAV target recognition accuracy, and reduce neural network parameters and computing costs. It can provide new ideas and solutions for the realization of element awareness tasks in a helicopter/UAV cooperative situation awareness system.
Neoliberalism Crisis and Fundamental Shifts in Africa’s Higher Education Provision in the 21st Century

This chapter explores the circumstances in the global south that have been reshaping teaching and learning practices, with particular focus on the African context. Anchored on a literature review strategy, the chapter explores some of the key pressure points in the higher education context that have been the trigger of reforms in the core practice of teaching and learning in recent years. In particular, the chapter discusses the neoliberalism crisis associated with the coronavirus pandemic in higher education, drawing attention to the inequalities that it ignited: not all students were affected in the same way; not all universities or colleges were affected in the same way; and not all students had the learning technologies required to carry on their education in the same way. Alongside the COVID-19 concerns, the chapter reflects on other pressure points for change including developments in digital technologies and the internet and changing students and changing higher education markets in sub-Saharan Africa. It argues that these forces are among a wave of influences that higher education institutions across the African continent cannot ignore. They form a blend of neoliberal reforms that are pressurising academics to change pedagogical models and threatening certain core values of a university: academic freedom, autonomy, and truth. The chapter develops the argument that although multiple pressures – arising from the pervasive influences of technology and the COVID-19 pandemic – are mounting on the higher education sector to reform its pedagogical practices, it should not be at the expense of perpetuating injustices, particularly among students.
APPLICATION OF THE QUANTUM LEARNING MODEL WITH MIND MAPPING METHOD IN HIGH SCHOOL STUDENTS

This study aims to determine 1) the application of the Quantum Learning learning model, 2) the differences in student interests and learning outcomes using the quantum learning model with the conventional model. This research is a quantitative descriptive type of research. The population of this study amounted to 240 students and a sample of 60 students. The experimental class has 30 students and the conventional class also has 30 students. The sampling technique is purposive sampling. Analysis of research data using descriptive statistical analysis, and infrensial analysis. The results showed that the experimental class students' interest in learning tended to be high with 82.97%. Meanwhile, the students' interest in learning in the control class tends to be low, with an average score of 54.85%. And the learning outcomes achieved by students on the subject matter of the atmosphere in the experimental class (taught using the quantum learning model) were in the good category, with an average of 76. The maximum value was 85 obtained and 65 the minimum value. Meanwhile for the control class ( taught by the teacher of the subject concerned using the lecture method) is in the sufficient category, with an average learning achievement of 62.8. The highest value achieved by students is 80 and the lowest value is 50