Reviewerly/exHarmony huggingface dataset:
Mobile Apps in Education: Focusing on the Anatomy of the Eye

In recent years there has been an accelerated technological development in all areas of life, including teaching and learning, especially in health sciences and Human Anatomy. This study offers an analysis of different mobile apps of human anatomy, specifically of the eye. We try to describe their main characteristics and compare them with each other. We highlight the main advantages: easy access, wide use, intuitive handling, currentness.
Influence of Information and Communication Technologies on Formation of Social Capital of the University

Empirical studies of social capital s a significant factor in improving the efficiency of Universities are very important for development of higher education. However, the practical measurement of the University's social capital and the evaluation of its impact on effectiveness is challenging. This paper discusses the problems of designing a theoretical model, measurement and analysis of the social capital of the University. This study presents the results of a pilot research aimed at developing the methodology and tools for measuring and analyzing the University's social capital. Qualitative and quantitative methods of analysis were adopted for this study. The units of analysis included teachers, Departments and Institutes of the University. The results of the study showed that empirical researches of the impact of social capital on the professional activities of University teachers is associated with a large number of factors, which requires the development of special algorithms and information systems for efficiency management based on the use of social capital.
Advanced Learning Architectures and Spatial Statistics for Beam Selection with Multi-Path

In this paper, we investigate the applicability of machine and deep learning (ML/DL) techniques to uplink beam selection problems with hybrid beamforming architectures. The goal is to select the element in the codebook of analog beamformers (ABF) yielding the highest sum-rate. The multi-antenna system operates in 5GNR's Frequency Range 2 (mmWave band). In this context, the presence of multi-path propagation along with the use of multi-carrier signals precludes the use of (single) angle-of-arrival information as an input to the learning system. Therefore, we investigate alternative statistics such as the singular vector/values of the (multi-carrier) channel matrix, the average covariance matrix, or the covariance matrix at a given subcarrier. Besides, we propose a novel ML/DL architecture enabling a continuous operation of the system which avoids the spectral efficiency losses associated to periodically switching to a dedicated ABF for the estimation of such statistics.
Does Skillful Use of Hard Information by Machines Outperform a Combination of Hard and Soft Information of Loan Officers in Lending Decisions?

We ask whether machine learning algorithms improve the efficiency of screening of the loan officers, and thereby help expand access to formal credit. We obtain loan application level data from an Indian bank. To overcome the selective labels problem, we exploit the incentive driven within officer difference in leniency within a calendar month. We find that the ML algorithm is able to lend 26.6% more at loan officers' delinquency rate or achieve 21.3% lower delinquency rate at loan officers' approval rate. Higher efficiency is achieved without compromising on equity.
A New Digital Multimedia Steganography Scheme Based on Complex Number Base

In this paper, we propose a new digital multimedia steganography scheme based on complex number base. Based on this idea, all kinds of digital information can be represented by 0-1 sequences, so there may be some relationship between the two sequences. In order to find this relationship, we focus on the coding of complex number and study the mapping function between points on the complex plane. The general process of our method can be summarized as follows: mapping the secret and public binary sequence into points on the complex plane, constructing a spline curve that goes through all secret and public points, and then, hiding the point differences in another public spline curve image for future secret message recovery. The experiment result shows the proposed method has relative stability and can accurately recover the secret information.
Computational estimation of point-spread-function field in axially symmetric optical systems

The Point-Spread-Function (PSF) field characterizes the geometric and diffractive properties of most optical system designs for imaging applications. The conventional method of computing the PSF field is carried out in two steps: first, geometrical computation of the wave aberration function, and then, diffraction evaluation. That method usually applies two approximations: (1) the exit and entrance pupil ray intersection coordinates are equal; and (2) the optical field amplitude distribution does not change between exit and entrance pupil planes. We propose here a variation of the conventional method that allows overcoming these two approximations in axially symmetric optical systems. To show the potentials of the method we applied it to a Cooke triplet. We analyse the influence of the number of traced rays on the measured accuracy of the method. We also evaluate the differences in the PSF estimation accuracy with respect to a method where the two aforementioned approximations were applied.
Rescoring Teacher Outputs with Decoded Utterances for Knowledge Distillation in Automatic Speech Recognition

Automatic Speech Recognition requires large amounts of training data to achieve good results. As this hand-labeling is both slow and expensive, the utilization of untranscribed speech has been explored for the task. This generally involves training a teacher-model on the available transcribed speech, and training a student model either directly on the latent representations of the teacher, or on the decoded output. We propose to combine these two approaches, and rescore the predictions of the teacher based on the decoded output. The probability of a decoded sentence, and how it corresponds with the probability distribution output of the teacher, affects the rescoring. When training our student model and evaluating using our proposed method, we find it gives up to 8.6% relative improvement in character error rate, and 5.4% relative improvement in word error rate over our strongest baseline.
Temporal and Multi-level Context Attentive GRU Neural Networks

With the popularity of internet lifestyle, various of mobile software are widely used to manage and record daily lives. However, providing personalized functions for travel data management, statistics and display for users is difficult. In this paper, we analyze the problems in the current personalized Point-of-interest (POI) recommendation algorithm, and on this basis, introduce a new neural network model. Our model applies the encoding-decoding framework based on GRU (Gate Recurrent Units). Besides, different attention mechanisms are also applied for considering the impact of contextual factors and check-in sequences for model prediction performance, including the temporal and multi-level context attention mechanisms. We have conducted two experiments with different check-in datasets. The result provided the better prediction performance of our model than several latest models.
Development of an Automated CGPBI Model Suitable for HEIs in India

An appropriate performance model indeed produces good results but not to dishearten commendable faculty with biased human intervention. This paper proposed a computer-generated performance-based index (CGPBI) model for a structural performance appraisal to faculty in suitable for Indian higher educational intuitions (HEI). Using the CGPBI model is exceptionally helpful in motivating the faculty who performed superior results in all aspects in the area of academics, research, and other contributions. This model uses Python to design a centralized platform to collect data related to faculty performance. A soft-computing model is developed to direct the methodology of data collections, creates a systematized data evaluation process, and provide data indices to take performance assessment judgments. This CGPBI model delivers spirited indices for competitive performances and motivates employees with more transparent results concerning their performance. This model recommends a computer-based multi-source assessment (MSA) mechanism that suggests computer-based logical proposition for evaluation of faculty annual performance appraisal through validation impartial variables in HEIs.
Hybrid Learning Approach Toward Situation Recognition and Handling

Abstract We propose a novel hybrid learning approach to gain situation awareness in smart environments by introducing a new situation identifier that combines an expert system and a machine learning approach. Traditionally, expert systems and machine learning approaches have been widely used independently to detect ongoing situations as the main functionality in smart environments in various domains. Expert systems lack the functionality to adapt the system to each user and are expensive to design based on each setting. On the other hand, machine learning approaches fail in the challenge of cold start and making explainable decisions. Using both of these approaches enables the system to use user’s feedback and capture environmental changes while exploiting the initial expert knowledge to solve the mentioned challenges. We use decision trees and situation templates as the core structure to interpret sensor data. To evaluate the proposed method, we generate a new human-annotated dataset simulating a smart environment. Our experiments show superior results compared with the initial expert system and the machine learning approach while preserving the initial expert system’s interpretability.
Visual Security Assessment via Saliency-Weighted Structure and Orientation Similarity for Selective Encrypted Images

Selective encryption has been widely used in image privacy protection. Visual security assessment is necessary for the effectiveness and practicability of image encryption methods, and there have been a series of research studies on this aspect. However, these methods do not take into account perceptual factors. In this paper, we propose a new visual security assessment (VSA) by saliency-weighted structure and orientation similarity. Considering that the human visual perception is sensitive to the characteristics of selective encrypted images, we extract the structure and orientation feature maps, and then similarity measurements are conducted on these feature maps to generate the structure and orientation similarity maps. Next, we compute the saliency map of the original image. Then, a simple saliency-based pooling strategy is subsequently used to combine these measurements and generate the final visual security score. Extensive experiments are conducted on two public encryption databases, and the results demonstrate the superiority and robustness of our proposed VSA compared with the existing most advanced work.
Translation Russian Cyrillic to Latin Alphabet using Random Forest Algorithm

Russian language is ranked as the sixth most spoken language in the world. It shows a high interest in the world population to use and learn the language. The Russian language has different alphabets from the Indonesian language, which is called Cyrillic. It has a different shape from the alphabet in general, which makes some obstacles to learn, understand and pronounce it.. Currently, technology is growing fast, and it changed people's lifestyles to be more practical and mobile so that the smartphone becomes one of the society's needs. Android is one of the most used operating systems in a smartphone nowadays, so the Capture to Translate was built in android-based. Capture to Translate is a media which is designed as a solution for those problems. The system was made based on image processing, the feature extraction process, and artificial intelligence using Random Forest algorithm classification with interface Android mobile application that utilizes the camera as an input device. This system has the highest accuracy of 93.33% at one syllable, 85% at two syllables, and 84.76 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> in 3 syllables.
Proposition and Practical Significance of Two Classes of New Teaching Methods and Diversified Assessment During the Coronavirus Disease 2019 Epidemic

After entering the new century, people are facing a new external environment. Recently human society is coming into knowledge economy era, and the industrial structure has deeply been adjusted. Today, the past teaching method of imparting knowledge and getting people to accept education, or exam-oriented teaching ideas have been unable to keep up with the needs of the development of the Times. In the future, the key point of education is to promote people to understand the knowledge, and grasp rules between things. Based on this, it is particularly necessary for the talents cultivation to adopt a new idea of training. The paper takes the major in mathematics as an example, designs two classes of new teaching methods CT&amp;CL and BOOF with the diversified assessment to cultivate students’ innovative thinking, providing a reference for the other students. The two classes of new teaching methods and the diversified assessment are worthy of further spreading and exercising during the coronavirus disease 2019 epidemic.
Support software for Automatic Speech Recognition systems targeted for non-native speech

Nowadays automatic speech recognition (ASR) systems can achieve higher and higher accuracy rates depending on the methodology applied and datasets used. The rate decreases significantly when the ASR system is being used with a non-native speaker of the language to be recognized. The main reason for this is specific pronunciation and accent features related to the mother tongue of that speaker, which influence the pronunciation. At the same time, an extremely limited volume of labeled non-native speech datasets makes it difficult to train, from the ground up, sufficiently accurate ASR systems for non-native speakers.
Production expert system to determine aircraft take-off and landing performance

Abstract This paper deals with the design of automated system for take-off and landing performance computing which is applicable to a wide range of aircrafts. The designed solution uses expert systems technology based on rules. The algorithm for logical inference of the expert system is developed.
Development of a mathematical model of the process of transferring landscape images through a noisy communication channel

The necessity of modeling systems for transmitting landscape images through communication channels is substantiated. Algorithms for transforming images for the modeling process have been developed. An algorithm for generating random noise in the communication channel based on the Poisson distribution has also been developed. A model has been developed that can be used to solve a variety of remote sensing data transmission problems.
Competing through Keyword Advertising

The impressive growth of online shopping has had a significant impact on firms’ strategies and customer behavior, bringing to the fore new forms of trademark exploitation that may affect competition. A prominent role is played by keyword advertising services provided by internet search engines. Keyword advertising systems have been the subject of several litigations with regard to the legality of the use of keywords which correspond to trademarks, since trademark holders complain that the essential functions of trademarks might be detrimentally affected. However, given the importance of search engines for attracting customers to the websites of retailers and competitors, online advertising restrictions also raise anticompetitive concerns on both sides of the Atlantic. Indeed, the E-commerce Sector Inquiry carried out by the European Commission reported that some retailers are limited in their ability to use or bid on the trademarks of certain manufacturers in order to get a preferential listing on search engines’ paid referencing service or are only allowed to bid on certain positions. Furthermore, the UK Competition and Markets Authority encountered brand-bidding restrictions in the markets for broadband, credit cards, energy, flights and home insurance, while the Netherlands Authority for Consumers & Markets analyzed the hotel sector. Moreover, in the US, the Federal Trade Commission has ruled that the largest online retailer of contact lenses unlawfully entered into a web of anticompetitive agreements with rivals, preventing them from bidding for search engine result advertisements that would inform consumers that identical products were available at lower prices. The aim of this paper is to shed light on the economic rationales and legal implications of keyword advertising in order to strike a proper balance between trademark protection and freedom of competition.
Deploying Fused Sharable Video Interaction Channels in Mobile Cloud

A plethora of mobile video applications involving all aspects of social life are becoming increasingly prevalent. However, for resource-hungry and delay-sensitive multi-view video applications such as multi-channel video conferencing and 3D videos, the hardware resources of mobile terminals becomes a bottleneck in concurrently decoding multiple videos. Fusing multiple views into a single-view video stream in the cloud before transmission can unload the computation of mobile terminals. But the delay increment caused by video fusing in this cloudbased multi-view video streaming makes the strategy of deploying such applications a new area to examine. In this paper, in the interest of deployment with higher resource utilization and less latency, first, together with a load model of the fused sharable video interaction process, a channel admission control algorithm that targets maximizing the supportive capacity of the cloud is introduced. Subsequently, a channel deployment algorithm based on load coordination between the cloud and mobile clients is proposed to ensure that the delay caused by cloud processing is acceptable while minimizing the terminal computing load.
Research on Security of Multi-access Edge Computing Nodes System Integrated by Blockchain Technology

This paper introduces the application mode of the multi-access edge computing nodes system (MECNS), and analyzes the needs of the security research of the MECNS. Aiming at the security problems faced by the MECNS in the application of new energy sources such as power plant-grid-load-store system (PPGLSS), etc., the security edge computing node (ECN) is defined, and the architecture of a MECNS with a security mechanism is designed. It is based on public and private key encryption algorithm. The authorization protocol, registration protocol, cancellation protocol and the address query protocol of ECNs are proposed. In this scheme, the confidentiality and security of communication between ECNs are realized through SSL. And on this basis, the blockchain technology is integrated to propose a trusted protection mechanism for edge computing. This architecture and related protocols can ensure the verifiability and non-repudiation of ECN activities.
Around the clock ‐ capsule networks and image transformations

Abstract Capsule networks are a promising new model class for image processing tasks. We investigate and compare their classification performance with convolutional neural networks on a new entry level image data set. In our experiments the capsules are able to learn the effect of rotated input images from data and outperform a comparable convolutional architecture. The results also show that convolutional and capsule networks need structural adjustment to respond to transformations that are not included in the training set.
Cryptocurrencies and the Velocity of Money

Velocity of money is central to the quantity theory of money, which relates it to the general price level. While the theory motivated countless empirical studies to include velocity as price determinant, few find a significant relationship in the short or medium run. Since the velocity of money is generally unobservable, these studies were limited to use proxy variables, leaving it unclear whether the lacking relationship refutes the theory or the proxies. Cryptocurrencies on public blockchains, however, visibly record all transactions, and thus allow to measure - rather than approximate - velocity. This paper evaluates most suggested proxies for velocity and also proposes a novel measurement approach. We introduce velocity measures for UTXO-based cryptocurrencies focused on the subset of the money supply effectively in use for the processing of transactions. Our approach thus explicitly addresses the hybrid use of cryptocurrencies as media of exchange and as stores of value, a major distinction in recently proposed theoretical pricing models. We show that each of the velocity estimators is approximated best by the simple ratio of on-chain transaction volume to total coin supply. Moreover, “coin days destroyed”, if used as an approximation for velocity, shows considerable discrepancy from the other approaches.
A program logic for fresh name generation

We present a program logic for Pitts and Stark's ν-calculus, an extension of the call-by-value simply-typed λ-calculus with a mechanism for the generation of fresh names. Names can be compared for equality and inequality, producing programs with subtle observable properties. Hidden names produced by interactions between generation and abstraction are captured logically with a second-order quantifier over type contexts. We illustrate usage of the logic through reasoning about well-known difficult cases from the literature.
Processing Oceanographic Data by Python Libraries Numpy, Scipy and Pandas

The study area is located in western Pacific Ocean, Mariana Trench. The aim of the data analysis is to analyze the potential influence of how various geological and tectonic factors may affect the geomorphological shape of the Mariana Trench. Statistical analysis of the data set in marine geology and oceanography requires an adequate strategy on big data processing. In this context, current re-search proposes a combination of the Python-based methodology that couples GIS geospatial data analysis. The Quantum GIS part of the methodology produces an optimized representative sampling dataset consisting of 25 cross-section profiles having in total 12,590 bathymetric observation points. The sampling of the geospatial dataset are located across the Mariana Trench. The second part of the methodology consists of statistical data processing by means of high-level programming language Python. Current research uses libraries Pandas, NumPy and SciPy. The data processing also involves the subsampling of two auxiliary masked data frames from the initial large data set that only consists of the target variables: sediment thickness, slope angle degrees and bathymetric observation points across four tectonic plates: Pacific, Philippine, Mariana, and Caroline. Finally, the data were analyzed by several approaches: 1) Kernel Density Estimation (KDE) for analysis of the probability of data distribution; 2) stacked area chart for visualization of the data range across various segments of the trench; 3) spacial series of radar charts; 4) stacked bar plots showing the data distribution by tectonic plates; 5) stacked bar charts for correlation of sediment thickness by profiles, versus distance from the igneous volcanic areas; 6) circular pie plots visualizing data distribution by 25 profiles; 7) scatterplot matrices for correlation analysis between marine geologic variables. The results presented a distinct correlation between the geologic, tectonic and oceanographic variables. Six Python codes are provided in full for repeatability of this research.