Reviewerly/exHarmony huggingface dataset:
English Phrase Speech Recognition Based on Continuous Speech Recognition Algorithm and Word Tree Constraints

This paper combines domestic and international research results to analyze and study the difference between the attribute features of English phrase speech and noise to enhance the short-time energy, which is used to improve the threshold judgment sensitivity; noise addition to the discrepancy data set is used to enhance the recognition robustness. The backpropagation algorithm is improved to constrain the range of weight variation, avoid oscillation phenomenon, and shorten the training time. In the real English phrase sound recognition system, there are problems such as massive training data and low training efficiency caused by the super large-scale model parameters of the convolutional neural network. To address these problems, the NWBP algorithm is based on the oscillation phenomenon that tends to occur when searching for the minimum error value in the late training period of the network parameters, using the K-MEANS algorithm to obtain the seed nodes that approach the minimal error value, and using the boundary value rule to reduce the range of weight change to reduce the oscillation phenomenon so that the network error converges as soon as possible and improve the training efficiency. Through simulation experiments, the NWBP algorithm improves the degree of fitting and convergence speed in the training of complex convolutional neural networks compared with other algorithms, reduces the redundant computation, and shortens the training time to a certain extent, and the algorithm has the advantage of accelerating the convergence of the network compared with simple networks. The word tree constraint and its efficient storage structure are introduced, which improves the storage efficiency of the word tree constraint and the retrieval efficiency in the English phrase recognition search.
Ontology generation for flight safety messages in air traffic management

Abstract The analysis of various data is a challenging issue in the field of air traffic flow management (ATFM). Managing a large volume of data and their correct interpretation plays a key role in the prevention of air accidents, human errors, and flight interactions. In general, pilots and flight navigators deal with massive and variety information, including flight safety messages, meteorological messages, aviation notes for air man (NOTAMs) and etc. Therefore relationship between Heterogeneous big data is problem. Recently, the semantic web has been recognized as a novel foundation of knowledge management that relies on ontology (concept modeling language). The present study aimed to propose an efficient infrastructure for ATMs by defining the concepts, rules, and relationships between the concepts related to the scope of flight information. To this end, we designed and implemented the ontology of flight safety messages, which included an important part of flight operation information. In this research, the internet protocol suite (IPS)-based aviation networks and methontology methodology were exploited for the engineering of the ontology of flight messages. In addition, the implementation process was carried out using the Protégé software. The analysis of the system was performed using the actual data of Mashhad Airport, Iran. Finally, the implementation of the ontology of flight messages was demonstrated after a case study of several flight examples. The developed ontology could be incorporated into the foundation of an aeronautical telecommunication network (ATN) aviation network as an application.
Exploring Contextual Importance and Utility in Explaining Affect Detection

By the ubiquitous usage of machine learning models with their inherent black-box nature, the necessity of explaining the decisions made by these models has become crucial. Although outcome explanation has been recently taken into account as a solution to the transparency issue in many areas, affect computing is one of the domains with the least dedicated effort on the practice of explainable AI, particularly over different machine learning models. The aim of this work is to evaluate the outcome explanations of two black-box models, namely neural network (NN) and linear discriminant analysis (LDA), to understand individuals affective states measured by wearable sensors. Emphasizing on context-aware decision explanations of these models, the two concepts of Contextual Importance (CI) and Contextual Utility (CU) are employed as a model-agnostic outcome explanation approach. We conduct our experiments on the two multimodal affect computing datasets, namely WESAD and MAHNOB-HCI. The results of applying a neural-based model on the first dataset reveal that the electrodermal activity, respiration as well as accelorometer sensors contribute significantly in the detection of “meditation” state for a particular participant. However, the respiration sensor does not intervene in the LDA decision of the same state. On the course of second dataset and the neural network model, the importance and utility of electrocardiogram and respiration sensors are shown as the dominant features in the detection of an individual “surprised” state, while the LDA model does not rely on the respiration sensor to detect this mental state.
Towards Efficient Video Detection Object Super-Resolution with Deep Fusion Network for Public Safety

Video surveillance plays an increasingly important role in public security and is a technical foundation for constructing safe and smart cities. The traditional video surveillance systems can only provide real-time monitoring or manually analyze cases by reviewing the surveillance video. So, it is difficult to use the data sampled from the surveillance video effectively. In this paper, we proposed an efficient video detection object super-resolution with a deep fusion network for public security. Firstly, we designed a super-resolution framework for video detection objects. By fusing object detection algorithms, video keyframe selection algorithms, and super-resolution reconstruction algorithms, we proposed a deep learning-based intelligent video detection object super-resolution (SR) method. Secondly, we designed a regression-based object detection algorithm and a key video frame selection algorithm. The object detection algorithm is used to assist police and security personnel to track suspicious objects in real time. The keyframe selection algorithm can select key information from a large amount of redundant information, which helps to improve the efficiency of video content analysis and reduce labor costs. Finally, we designed an asymmetric depth recursive back-projection network for super-resolution reconstruction. By combining the advantages of the pixel-based super-resolution algorithm and the feature space-based super-resolution algorithm, we improved the resolution and the visual perception clarity of the key objects. Extensive experimental evaluations show the efficiency and effectiveness of our method.
Quantitative Analysis of Programs with Probabilities and Concentration of Measure Inequalities

The quantitative analysis of probabilistic programs answers queries involving the expected values of program variables and expressions involving them, as well as bounds on the probabilities of assertions. In this chapter, we will present the use of concentration of measure inequalities to reason about such bounds. First, we will briefly present and motivate standard concentration of measure inequalities. Next, we survey approaches to reason about quantitative properties using concentration of measure inequalities, illustrating these on numerous motivating examples. Finally, we discuss currently open challenges in this area for future work.
Higher education strategies for the 21st century: philosophical foundations and the humanist approach

The paper is dedicated to a philosophical consideration of the transformation strategies for higher education, particularly those revealed by the current Covid-19 pandemic. The situation of the latter is presented as a particular form of the general VUCA characteristics of the today’s world of uncertainty, unpredictability and qualitative complexity (‘supercomplexity’). It is argued that the main result of our acknowledgement of the VUCA situation in the world for higher education is the image of the developed human personality becoming the main value and at the same time the main goal of higher education. Under the conditions of volatility and ambiguity, any existing ‘ready-made’ knowledge, as well as any instructions for effective rational behavior, turns to be inadequate. Because of that, it is no longer sufficient to have a set of ‘competences’ as a final result of the educational process at today’s university. Higher education has to turn to developing in its graduates certain multidisciplinary qualities, like critical independent thinking and ability to create one’s own knowledge, up to aiming at an all-round development of cultured personality. The paper argues that the education of critical and creative thinking is closely related to the transition to student-centered learning, as each individual student is to become a full-powered subject of the educational process according to one’s own interests, abilities and curricula, with the role of a teacher starting to resemble that of a moderator, the one who is to help his or her undergraduates to navigate through the vast ocean of available information in order for them to choose and to create their own, personal knowledge. The latter task is especially enforced by the distant and online learning having become popular during the Covid-19 pandemic: that form of learning puts especially high demands on self-discipline and self-responsibility of a student’s personality, while presenting itself as rather a supplement than a replacement to more traditional forms of higher education with personal communication between student and teacher.
Configuring Devices for Phenomena in-the-Making

STS scholars are engaging in collaborative research in order to study extended socio-technical phenomena. This article participates in discussions on methodography and inventive methods by reflecting on visualizations used both internally by a team of researchers and together with study participants. We describe how these devices for generating and transforming data were brought to our ethnographic inquiry into the formation of research infrastructures which we found to be unwieldy and evolving phenomena. The visualizations are partial renderings of the object of inquiry, crafted and informed by ‘configuration’ as a method of assemblage that supports ethnographic study of contemporary socio-technical phenomena. We scrutinize our interdisciplinary bringing together of visualizing devices - timelines, collages, and sketches - and position them in the STS methods toolbox for inquiry and invention. These devices are key to investigating and engaging with the dynamics of configuring infrastructures intended to support scientific knowledge production. We conclude by observing how our three kinds of visualizing devices provide flexibility, comprehension and in(ter)ventive opportunities for study of and engagement with complex phenomena in-the-making.
A Huffman-Based Joint Compression and Encryption Scheme for Secure Data Storage Using Physical Unclonable Functions

With the developments of Internet of Things (IoT) and cloud-computing technologies, cloud servers need storage of a huge volume of IoT data with high throughput and robust security. Joint Compression and Encryption (JCAE) scheme based on Huffman algorithm has been regarded as a promising technology to enhance the data storage method. Existing JCAE schemes still have the following limitations: (1) The keys in the JCAE would be cracked by physical and cloning attacks; (2) The rebuilding of Huffman tree reduces the operational efficiency; (3) The compression ratio should be further improved. In this paper, a Huffman-based JCAE scheme using Physical Unclonable Functions (PUFs) is proposed. It provides physically secure keys with PUFs, efficient Huffman tree mutation without rebuilding, and practical compression ratio by combining the Lempel-Ziv and Welch (LZW) algorithm. The performance of the instanced PUFs and the derived keys was evaluated. Moreover, our scheme was demonstrated in a file protection system with the average throughput of 473Mbps and the average compression ratio of 0.5586. Finally, the security analysis shows that our scheme resists physical and cloning attacks as well as several classic attacks, thus improving the security level of existing data protection methods.
Neural Estimator of Information for Time-Series Data with Dependency

Novel approaches to estimate information measures using neural networks are well-celebrated in recent years both in the information theory and machine learning communities. These neural-based estimators are shown to converge to the true values when estimating mutual information and conditional mutual information using independent samples. However, if the samples in the dataset are not independent, the consistency of these estimators requires further investigation. This is of particular interest for a more complex measure such as the directed information, which is pivotal in characterizing causality and is meaningful over time-dependent variables. The extension of the convergence proof for such cases is not trivial and demands further assumptions on the data. In this paper, we show that our neural estimator for conditional mutual information is consistent when the dataset is generated with samples of a stationary and ergodic source. {In other words, we show that our information estimator using neural networks converges asymptotically to the true value with probability one. Besides universal functional approximation of neural networks, a core lemma to show the convergence is Birkhoff's ergodic theorem. Additionally, we use the technique to estimate directed information and demonstrate the effectiveness of our approach in simulations.
Improved Relief Weight Feature Selection Algorithm Based on Relief and Mutual Information

As the classic feature selection algorithm, the Relief algorithm has the advantages of simple computation and high efficiency, but the algorithm itself is limited to only dealing with binary classification problems, and the comprehensive distinguishing ability of the feature subsets composed of the former K features selected by the Relief algorithm is often redundant, as the algorithm cannot select the ideal feature subset. When calculating the correlation and redundancy between characteristics by mutual information, the computation speed is slow because of the high computational complexity and the method’s need to calculate the probability density function of the corresponding features. Aiming to solve the above problems, we first improve the weight of the Relief algorithm, so that it can be used to evaluate a set of candidate feature sets. Then we use the improved joint mutual information evaluation function to replace the basic mutual information computation and solve the problem of computation speed and correlation, and redundancy between features. Finally, a compound correlation feature selection algorithm based on Relief and joint mutual information is proposed using the evaluation function and the heuristic sequential forward search strategy. This algorithm can effectively select feature subsets with small redundancy and strong classification characteristics, and has the excellent characteristics of faster calculation speed.
Methods and tools for analysing students’ digital footprint in the course of work under educational programmes

Collecting and analysing a student’s digital footprint is an integral part of the Russian education development in the conditions of the digital economy. This activity is connected, among the other things, with the assessment of the students’ formed competencies in the course of blended learning in the educational process. The purpose of the research is development of approaches, methods and tools for analysing the data generated in the digital educational environment (DEE), with a view to assessing the correlation between the structure and content of educational programmes and the results of the students’ educational activities under these programmes. The methodology of the essay is based on the Educational Data Mining (EDM), along with the methods for design and development of information systems and databases. The following data processing methods were used: correlation analysis and Distillation of Data for Human Judgment. The analysis used extensive detailed data on various aspects of the educational process, accumulated for over more than 10 years on the basis of Herzen State Pedagogical University of Russia. The authors have developed a tool for collecting and analysing the students’ digital footprint in a blended learning environment, using EDM methods. A series of calculations was made in accordance with the EDM methods, that make it possible to draw preliminary conclusions about the nature of relationship of some selected indicators (number of actions during the course, students’ activity in the course chat(s), final grade for the course work, attendance time at the course), obtained as part of the student work in the Moodle distance teaching system (DTS), with the interim certification results. For instance, the extent of interrelation between the final e-learning course grade and the results of interim certification is 0.7108, which is evidence of close interdependence. The extent of interrelation between the scope of course activities and the interim certification grade (0.314) shows only slight interdependence. Such calculations serve a basis for the assumptions in respect of assessing the impact of student activities on academic success, on the efficiency of e-learning courses and recommendations for modifying the educational resources with a view to improving their quality. The experience, approaches and results presented by the authors testify to expediency of using EDM methods for the analysis of the educational process.
Learning Accurate Integer Transformer Machine-Translation Models

We describe a method for training accurate Transformer machine-translation models to run inference using 8-bit integer (INT8) hardware matrix multipliers, as opposed to the more costly single-precision floating-point (FP32) hardware. Unlike previous work, which converted only 85 Transformer matrix multiplications to INT8, leaving 48 out of 133 of them in FP32 because of unacceptable accuracy loss, we convert them all to INT8 without compromising accuracy. Tested on the newstest2014 English-to-German translation task, our INT8 Transformer Base and Transformer Big models yield BLEU scores that are 99.3–100% relative to those of the corresponding FP32 models. Our approach converts all matrix-multiplication tensors from an existing FP32 model into INT8 tensors by automatically making range-precision trade-offs during training. To demonstrate the robustness of this approach, we also include results from INT6 Transformer models.
Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction

Methods to quantify the complexity of trajectory datasets are still a missing piece in benchmarking human trajectory prediction models. In order to gain a better understanding of the complexity of trajectory prediction tasks and following the intuition, that more complex datasets contain more information, an approach for quantifying the amount of information contained in a dataset from a prototype-based dataset representation is proposed. The dataset representation is obtained by first employing a non-trivial spatial sequence alignment, which enables a subsequent learning vector quantization (LVQ) stage. A large-scale complexity analysis is conducted on several human trajectory prediction benchmarking datasets, followed by a brief discussion on indications for human trajectory prediction and benchmarking.
Dataset for the Solar Incident Radiation and Electricity Production BIPV/BAPV System on the Northern/Southern Façade in Dense Urban Areas

The prosperous implementation of Building Integrated Photovoltaics (BIPV), as well as Building Attached Photovoltaics (BAPV), needs an accurate and detailed assessment of the potential of solar irradiation and electricity production of various commercialised technologies in different orientations on the outer skins of the building. This article presents a dataset for the solar incident radiation and electricity production of PV systems in the north and south orientations in a dense urban area (in the northern hemisphere). The solar incident radiation and the electricity production of two back-to-back PV panels with a ten-centimetre gap for one year are monitored and logged as primary data sources. Using Microsoft Excel, both panels’ efficiency is also presented as a secondary source of data. The implemented PV panels are composed of polycrystalline silicon cells with an efficiency of 16.9%. The results depicted that the actual efficiency of the south-facing panel (13%–15%) is always closer to the standard efficiency of the panel compared to the actual efficiency of the north-facing panel (8%–12%). Moreover, although the efficiency of the south-facing panel on sunny days of the year is almost constant, the efficiency of the north-facing panel decreases significantly in winter. This phenomenon might be linked to the spectral response of the polycrystalline silicon cells and different incident solar radiation spectrum on the panels. While the monitored data cover the radiation and system electricity production in various air conditions, the analysis is mainly conducted for sunny days, and more investigation is needed to analyse the system performance in other weather conditions (like cloudy and overcast skies). The presented database could be used to analyse the performance of polycrystalline silicon PV panels and their operational efficiency in a dense urban area and for different orientations.
The Varying Threshold Values of Logistic Regression and Linear Discriminant for Classifying Fraudulent Firm

The aim of the research is to find the best performance both of logistic regression and linear discriminant which their threshold uses some various values.The performance tools used for evaluating classifier model are confusion matrix, precision-recall, F1 score and receiver operation characteristic (ROC) curve.The Audit-risk data set are used for the implementation of the proposed method.The screening data and dimension reduction by using principal component analysis (PCA) are the first step that must be conducted before the data are divided into the training and testing set.After the training process for obtaining the classifier model parameters has been completed, the calculation of performance measures is done only on the testing set where the various constants are added to the threshold value of both classifier models.The logistic regression classifier has the best performance of 94% on the precision-recall, 91.7% on the F1-score, and 0.906 on the area under curve (AUC) where the threshold values are on the interval between 0.002 and 0.018.On the other hand, the linear discriminant classifier has the best performance when the threshold value is 0.035 and its performance value is respectively the precision-recall of 94%, the F1-score of 91.7%, and the AUC of 0.846.