Reviewerly/exHarmony huggingface dataset:
IEEE TCCN Special Section Editorial: Machine Learning and Artificial Intelligence for the Physical Layer

We are delighted to introduce this special section of the IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (TCCN), which aims at exploring recent advances and addressing practical challenges in the applications of machine learning (ML) and artificial intelligence (AI) for physical layer design and optimization. We have received a total number of 36 submissions, and after a rigorous review process, 12 articles have been selected for publication, which are briefly discussed as follows.
A Study on Customer Satisfaction of Mobile Shopping Apps Using Topic Analysis of User Reviews

Despite the rapid growth of the mobile shopping market, major market participants are continuing to suffer operating losses due to severe competition. To solve this problem, the mobile shopping market requires research to improve customer satisfaction and customer loyalty rather than excessive competition. However, the existing studies have limits to reflect the direct needs of customers because they extract the factors on the basis of the Technology Acceptance Model and the literature study. In this study, to reflect the direct requirements of users of mobile shopping Apps, we derived concretely and various factors influencing customer satisfaction through a topic analysis using user reviews. And then we assessed the importance of derived factors to customer satisfaction and analyzed the effects of customer satisfaction on customer complaints and customer loyalty on a structural equation model based on the American customer satisfaction index. We expect that our framework linking a topic analysis and a structural equation model is to be applicable to studies on the customer satisfaction of other mobile services.
Design Principles of Mobile Learning Frameworks

Numerous studies show positive cognitive and affective results regarding the adoption of mobile learning; however, adoption levels are low, and when mobile learning occurs, it is not always based on innovative pedagogies. The main objective of this study is to identify and analyse key design principles to develop a model for the adoption of mobile learning in education. This research is based on a systematic review of 20 publications. The findings reveal that most of the current studies focus on the adoption of mobile learning and the design and development of systems and applications. Additionally, these are mostly aimed at educators and instructional designers. Finally, the main dimensions that support the theoretical frameworks are the collaborative, social and communicative, contextual and spatial, pedagogical, technological, and strategic dimensions. Based on these findings, this study presents seven design principles for the adoption of mobile learning.
Use of Optional Data Curation Features by Users of Harvard Dataverse Repository

Objective: Investigate how different groups of depositors vary in their use of optional data curation features that provide support for FAIR research data in the Harvard Dataverse repository. Methods: A numerical score based upon the presence or absence of characteristics associated with the use of optional features was assigned to each of the 29,295 datasets deposited in Harvard Dataverse between 2007 and 2019. Statistical analyses were performed to investigate patterns of optional feature use amongst different groups of depositors and their relationship to other dataset characteristics. Results: Members of groups make greater use of Harvard Dataverse's optional features than individual researchers. Datasets that undergo a data curation review before submission to Harvard Dataverse, are associated with a publication, or contain restricted files also make greater use of optional features. Conclusions: Individual researchers might benefit from increased outreach and improved documentation about the benefits and use of optional features to improve their datasets' level of curation beyond the FAIR-informed support that the Harvard Dataverse repository provides by default. Platform designers, developers, and managers may also use the numerical scoring approach to explore how different user groups use optional application features.
Dense and Sparse 3D Deformation Signatures for 3D Dynamic Face Recognition

This work analyses dense and sparse 3D Deformation Signatures to represent 3D temporal deformation instances. The signatures are employed in dynamic 3D face recognition, however, they are applicable in other domains. This is demonstrated for dynamic expression recognition. The pushing need for non-intrusive bio-metric measurements made face and its expressions recognition dominant players in domains like entertainment, surveillance and security. The proposed signature can be computed from 2D, 3D or hybrid input by means of robust 3D fitting. It is computed given a non-linear 6D space representation which guarantees by construction physically plausible 3D deformations. A unique deformation indicator is computed per triangle in a triangulated mesh as a ratio derived from scale and in-plane deformation in the canonical space. These indicators are concatenated densely or sparsely to form the signature. It is then used to learn the 3D deformation space from the temporal facial signals. Two dynamic datasets were examined for evaluation. The reported 1-Rank recognition accuracy outperforms the existing literature. Democratising the recognition step results in 100% accuracy as demonstrated by the reported confusion matrices. In an open-world setting in the face recognition context, an accuracy of 100% was achieved in detecting intruders. The signature robustness has been further validated in face expressions recognition from a very challenging highly 3D dynamic dataset.
Software Quality Assurance and Android Application Development: A Comparison among Traditional and Agile Methodology

This paper focuses on software quality assurance in android application development with focus on approaches to confirm software quality in android applications. In this work, we compare the waterfall model with agile-scrum methodologies to realize software quality assurance with special focus on mobile applications. This research allows us to answer, “Which process model and practices are available for android software development along with focus on quality assurance?” The other research focus in this paper is “How auditors, developers and testers play their defined roles to maintain quality in mobile application development?” Agility for software development has transformed into a preferred and favored method for mobile application development. The major aim of this research is to contribute for a better knowledge for mobile application development methodologies practiced by the organizations for the development of applications based on systematic literature review.
ivpy: Iconographic Visualization Inside Computational Notebooks

Iconographic Visualization in Python, or ivpy, is a software module, written in the Python programming language, that provides a set of functions for organizing iconographic representations of data, including images and glyphs. The module also provides methods for extracting visual features from images; generating and hand-tuning clusters of data points; and embedding high-dimensional data in 2D coordinate spaces. It is designed for use inside computational notebooks, so that users working with data needn't leave the notebook environment in order to generate visualizations. The software is designed primarily for those researchers working with large image datasets in fields where human visual expertise cannot be replaced with or superseded by machine vision, such as art history and media studies.
Multi-agent communication models for cooperative navigation in complex environments

Multi-agent navigation in restricted environments presents significant challenges, since agents need to move to their goals in an efficient manner while avoiding collisions with both static and dynamic obstacles. Previously, C-Nav was proposed as a method that can effectively coordinate groups of agents in very restricted environments. In this work, we propose and evaluate three alternative communications models for C-Nav, that intend to provide more flexibility to the original method. The results of our experiments show that each of our proposed methods can lead to significant improvements over C-Nav in specific types of restricted environments.
Correction to: Technology Acceptance of a Machine Learning Algorithm Predicting Delirium in a Clinical Setting: a Mixed-Methods Study

A Correction to this paper has been published: https://doi.org/10.1007/s10916-021-01728-5
Pre-processing of Signature Images for Detection and Verification

The companies receive a lot of scanned or photographed documents from their customers. Therefore, an important technology is to automatically detect the signatures on the documents and then match them with the customer's signature in the system for verification. However, since overlapping signature and stamp makes it harder to solve the problem, some preprocessing operations are necessary. The object detection models that are used for detecting the signatures on the documents could not perform well when they are trained with clean signature images. To address this problem, in this study a new dataset is generated by combining some fake stamps with clean signatures to train the model with them. In the next step, the detected stamped signatures are matched with the clean reference signatures. However, due to stamps, this process cannot be performed successfully. For this reason, in order to remove the stamp, an image processing system is developed. Obtained results show the capability of image processing techniques for the challenging problem of cleaning the stamped signature image.
TEI-friendly annotation scheme for medieval named entities: a case on a Spanish medieval corpus

Abstract Medieval documents are a rich source of historical data. Performing named-entity recognition (NER) on this genre of texts can provide us with valuable historical evidence. However, traditional NER categories and schemes are usually designed with modern documents in mind (i.e. journalistic text) and the general-domain NER annotation schemes fail to capture the nature of medieval entities. In this paper we explore the challenges of performing named-entity annotation on a corpus of Spanish medieval documents: we discuss the mismatches that arise when applying traditional NER categories to a corpus of Spanish medieval documents and we propose a novel humanist-friendly TEI-compliant annotation scheme and guidelines intended to capture the particular nature of medieval entities.
Optical-electronic implementation of artificial neural network for ultrafast and accurate inference processing

With the rapid development of integrated nanophotonics technology, the circuit architecture for optical neural networks that construct neural networks based on integrated nanophotonics has been widely investigated. Since the optical neural network can complete the inference processing just by propagating the optical signal in the network, the inference processing by the optical neural network is expected more than one order of magnitude faster than the electronics-only implementation of an artificial neural network (ANN). In this paper, we first propose an optical vector-matrix multiplication (VMM) circuit using wavelength division multiplexing, which enables inference processing with ultra-wideband. Although the optical VMM circuit is extremely fast, the initial version is designed for fully connected network structures, which consume large amounts of power in laser light sources. As a solution to this power explosion, this paper especially proposes sparsely connected network structures for the optical VMM operation, which reduces the power consumed in the laser sources by three orders of magnitude without any speed and bandwidth degradation compared with the fully connected counterpart. Although the main part of ultra-fast ANN is VMM, batch normalization and activation function are integral parts for accurate inference processing in ANN. Batch normalization applied at inference processing is a technique for improving the inference accuracy of ANN. Without batch normalization and activation function, the inference accuracy of ANN may significantly degrade. In this paper, we next propose electronic circuit implementation of batch normalization and activation function, which significantly improves the accuracy of inference processing without sacrificing the speed performance of inference processing. Those two functions can be implemented based on an energy-efficient O-E-O repeater circuit. We specifically propose the electronic implementation of Exponential Linear Unit (ELU in short) as an activation function. It is known that ELU largely contributes to improving the inference accuracy of ANN as well as learning speed. Finally, in this paper, we demonstrate the ultra-fast and accurate operation of the optical-electronic ANN circuit using TensorFlow and optoelectronic circuit simulator.
Education And Intelligentnost’: A Comparative Analysis

The paper touches upon an issue of intelligentnost' and education formation in modern society which is at the moment undergoing the period when traditional Russian spiritual and moral values are being eroded.Social rationalization is one of the reasons that give rise to intelligentnost' as a personal quality.Individual's moral values strive for a greater amount of knowledge, constant self-improvement and selfdevelopment, as well as self-consciousness, are the prerequisites for intelligentnost' to originate.On the one hand, it is an ideal quality; on the other, it is a real quality of a person, it is an individual development track.Thus, there is both an ideal understanding of this quality and its practical manifestation.Education is not just an amount of knowledge, but a striving and a need for its accumulation as well as a capability to do that.An ability to reflect upon life and highly developed worldviews are indispensable attributes of education.When acquiring knowledge and skills, an educated person should establish their system of values, master a critical and creative approach to solving scientific and everyday problems, exercise selfcognition and self-observation.Education is not the only cause that entails reflection.It might be triggered by life circumstances that make people start thinking about the meaningfulness of their existence.Intelligentnost' and education are the qualities that contribute to the development of a new system of ideals that will be based on traditional Russian spiritual and moral values.
Multimodal News Feed Evaluation System with Deep Reinforcement Learning Approaches

Multilingual and multimodal data analysis is the emerging news feed evaluation system. News feed analysis and evaluations are interrelated processes, which are useful in understanding the news factors. The news feed evaluation system can be implemented for single or multilingual language models. Classification techniques used on multilingual news analysis require deep layered learning techniques rather than conventional approaches. In this proposed work, a hierarchical structure of deep learning algorithms is implemented for making an effective complex news evaluation system. Deep learning techniques such as the Deep Cooperative Multilingual Reinforcement Learning Model, the Multidimensional Genetic Algorithm, and the Multilingual Generative Adversarial Network are developed to evaluate a vast number of news feeds. The proposed tech-niques collaborate in a pipeline order to build a deep news feed evaluation system. The implementation details project that the newly proposed system performs 5% to 12% better than the other news evaluation systems.
The Implementation of Zoom Cloud Meeting on Choir Learning in the Covid-19 Pandemic

This research based of the COVID-19 pandemic which makes choir activities must change the direct learning system into online learning.This study aims to describe online choir learning using Zoom Cloud Meeting based on the learning strategies and find out the role of Zoom Cloud Meeting on the self-regulated in choir learning at Swara Wadhana Universitas Negeri Yogyakarta Student Choir.The research subjects were 30 members of the Swara Wadhana Univeritas Negeri Yogyakarta Student Choir.This study uses a qualitative approach with qualitative descriptive research methods.Collecting data using interview techniques, observation, and documentation.Data analysis using an interactive model from Miles & Huberman which includes the steps: data reduction, data presentation, and drawing conclusions.The description of choir learning is obtained from the learning strategy: pre instructional activities, the material presentation, student participation, tests and follow-up.The indicators of self-regulated learning used in the study are independence of others, self-confidence, disciplined, responsibility, initiative, and self-control.The results showed that the cloud meeting zoom can be used as a medium for implementing choir learning in the pandemic era with the adjustment of learning strategies by the trainer; and can support self-regulated learning.The implication of this research show that independence of other-indicator can be considered and be further investigated in the online choir learning.
More on Diophantine Equations

In this paper, we will find the solutions of many Diophantine equations.Some are of the form 2(3 x )+ 5(7y ) +11=z2 for non negative x,y and z. we also investigate solutions ofthe Diophantine equation 2(x+3) +11(3y ) ─ 1= z2 for non negative x,y and z and finally, westudy the Diophantine equations (k!×k)n = (n!×n)k and ( 𝒌! 𝒌 ) 𝒏 = ( 𝒏! 𝒏 ) 𝒌 where k and n are positive integers. We show that the first one holds if and only if k=n and the second one holds if and only if k=n or (k,n) =(1,2),(2,1).We also investigate Diophantine equation u! + v! = uv and u! ─ v! = uv .
A Survey of Cost Optimization in Serverless Cloud Computing

Abstract Recently serverless cloud computing which was proposed by Amazon in 2015 is getting more favored by developers because of its charging model of pay-by-usage, applicability for fine-grained services and transparency of the servers to developers. Although a lot of study has proved that the performance and cost of serverless platform is more ideal than the traditional cloud computing service, the new metering model of serverless which depends on execution time, usage count and memory footprint is still unfamiliar to most developers and researchers. In this paper, we firstly make an introduction to the new metering model and the advantages of serverless over traditional cloud services. Then according to the cost affecting factors, we make a comprehensive survey of some constraints that mainly impact the cost of serverless and related methods to reduce cost in three levels—function level, container level and cloud platform level. Based on the analysis, we also propose some directions worthy of in-depth study in the future.
On the English Classroom Teaching Mode of Primary School under the Background of Distance Education

Primary school internet plus education is also a new trend in the implementation of the English Teaching in the Internet plus. With the high attention of the party and the state on Rural Education in remote areas, the distance education system under the background of Internet plus has been maturing. Therefore, this study aims to explore a new model for the implementation of primary English classroom teaching through the way of "Internet plus". New teaching methods, new teaching means, new positioning of teachers' role, new ways of teachers' interaction, new ways of learning and new ways of classroom evaluation.
Image Forensics using Optimal Normalization in Challenging Environment

Digital images are becoming the backbone of the social platform. To day of life of the people, the high impact of the images has raised the concern of its authenticity. Image forensics need to be done to assure the authenticity. In this paper, a novel technique is proposed for digital image forensics. The proposed technique is applied for detection of median, averaging and Gaussian filtering in the images. In the proposed method, a first image is normalized using optimal range to obtain a better statistical information. Further, difference arrays are calculated on the normalized array and a proposed thresholding is applied on the normalized arrays. In the last, co-occurrence features are extracted from the thresholding difference arrays. In experimental analysis, significant performance gain is achieved. The detection capability of the proposed method remains upstanding on small size images even with low quality JPEG compression.
Uncertainty-aware Path Planning Using Reinforcement Learning and Deep Learning Methods

This paper proposes new algorithms to improve Reinforcement Learning (RL) and Deep Q Network (DQN) methods for path planning considering uncertainty in the environment's perception. The authors aim to formulate and solve the path planning optimization problem by minimizing the path length, avoiding obstacles, and minimizing the related uncertainty. In this regard, a reward function is constructed based on a weighted feature of the environment images, robot and environment constraints, and path optimality criterion as the optimization problem's objective function. In this study, Deep Learning (DL) is used for two purposes. First, for perceiving a real environment to find the state transition matrix of the mobile robot path planning problem, and second, for extracting the state’s features directly from an image of the environment to select the appropriate actions. To solve the path planning problem, it is formed in the context of an RL problem, and a Convolutional Neural Network (CNN) is used to approximate Q-values as a linear parameterized function. Implementing this approach improves the Q-learning, SARSA, and DQN algorithms as the new versions, called POQL, POSARSA, and PODQN. The learning process results show that using newly improved algorithms increases path planning performance by more than 20%, 21%, and 5% compared to the Q learning, SARSA, and DQN, respectively.
Fixed-Length Coding for Escape Samples in Palette Mode

Palette mode is a powerful tool for screen content coding in the upcoming versatile video coding (VVC) standard. In the palette mode, escape samples are employed to handle an outlier case. In this paper, a quantization parameter (QP) dependent fixed-length binarization is proposed for escape samples coding to 1) simplify the design and 2) improve coding efficiency. The length is calculated according to the QP for the current block and correspondingly, the dequantization process can be also implemented by only left shifting. The proposed method is evaluated with VVC reference software VTM-6.0 on typical sequences containing "text and graphics with motion". Experimental results report that the proposed scheme can achieve up to 2.0% BD-rate savings compared to VTM-6.0 with a much simpler design.