Reviewerly/exHarmony huggingface dataset:
Comparing Written and Voice Captured Responses of the User Experience Questionnaire (UEQ)

Standardized questionnaires are widely used instruments to evaluate UX and their capture mechanism has been implemented in written form, either on paper or in digital format. This study aims to determine if the UX evaluations obtained in the standardized UEQ questionnaire (User Experience Questionnaire) are equivalent if the response capture mechanism is implemented using the traditional written form (digitally) or if a conversational voice interface is used. Having a UX evaluation questionnaire whose capture mechanism is implemented by voice could provide an alternative to collect user responses, preserving the advantages present in standardized questionnaires (quantitative results, statistically validated, self-reported by users) and adding the ease of use and growing adoption of conversational voice interfaces. The results of the case study described in this paper show that, with an adequate number of participants, there are no significant differences in the results of the six scales that make up UEQ when using either of the two response capture mechanisms.
Using Clean Architecture Principles to Improve the Design and Implementation of the Mobiles Online Platform

Mobiles Online is a client–server application that is used to turn “ordinary” web pages into ones suitable for visualization on mobile devices. It is mainly used for legacy web pages, which for pragmatic reasons are expensive and time-consuming to rewrite with adaptive or responsive design. After building the initial prototype of the application, new requirements and functionalities emerged, which proved to be difficult to implement. The problem arose of choosing a more appropriate architecture to minimize code dependencies and facilitate the development of new functionalities. The paper presents the architecture of the Mobiles Online application, based on the concept of the “clean architecture” model. The main goal is to separate and eliminate the dependencies between the modules of an application. An important emphasis is the separation of the application core from other modules and the independence of their functions. This architecture provides flexibility and easy scalability to implement future functions.
Ideas about the values and meanings of the profession «teacher» at different levels of pedagogical education

Introduction. In the system of pedagogical education, the training of professionals is possible at several levels, but the logic, concept, and basis of training should be unified and pass through all levels of education, but the normative legal acts underlying each level are mostly not synchronized with each other. Overcoming the existing contradictions is seen through the implementation of an axiological approach in education, in which value orientations should determine the meaning and content of education. The hypothesis of the study is based on the assumption that there are significant differences in the values of pupils studying at different levels of pedagogical education. To overcome the existing gap, the training of future teachers should be based on common value guidelines and educational results. Materials and Methods. The purpose of the work is to determine the value orientations of pedagogical activity from the point of view of the students of the pedagogical college and students of the pedagogical university. Sample: 400 people participated in the online study: 147 students of the pedagogical college and 253 students of the pedagogical university. Methods: the author's methodology is a questionnaire of values laid down by educational and professional standards. The structure of the methodology consists of three lists of values (relation, qualities, knowledge), reflecting the value orientations associated with the target objects – the child, the educational environment and the teaching profession. Results. Students of both levels of pedagogical education at the top of the subjective rating of values have the professional activity of a teacher for society. Otherwise, both groups of study participants have all the values expressed equally, but they put different personal meaning into them. For college students, the methodological literacy of a specialist is in the first place, the instrumental basis of the profession, which is expressed in the main thing – the teaching methodology and the possession of various methods and methods of teaching. And university students are more focused on the areas of "educational environment" and "profession". Discussion and Conclusions. The obtained data indicate the following problems: a low level of subjectivity of the child and the development of the student's personal potential for the future teacher. The solution to these problems is seen in ensuring the equivalent translation of values belonging to the category "child-environment-profession" and "qualities-relations-knowledge". The diagnosed value gaps must be leveled by combining the requirements and results laid down by educational standards and implemented in learning technologies.
Need for Teachers' Professional Development in a Low-Resource Context During and After COVID-19

This qualitative study investigated teachers’ professional development (TPD) needs for teaching marginalised children who do not have access to virtual platforms in a Low and Middle Income Country (LMIC). Data were gathered using semi-structured interview schedules from different stakeholders of primary education in Bangladesh. The findings revealed that teachers have taken additional measures to online remote teaching to continue marginalised children’s education. The study also showed that teachers need to develop new skills for doing so and they took several innovative initiatives in addition to government and school provided professional development provisions to address the needs. The study concluded by indicating further scopes of TPD for remote and blended learning.
Radiation emitter signal recognition based on VMD and IALO-SVM

With the continuous development of modern electronic warfare technology, the enemy radar signals captured in complex environments usually have very few useful signals. In order to improve the recognition accuracy and anti-noise performance of radar emitter signal(RES), In this paper, an RES identification method based on variational mode decomposition (VMD) and improved ant lion optimization (IALO) is proposed to optimize support vector machine(SVM) parameters. Firstly, the VMD algorithm is used to decompose the radar signal into six intrinsic mode functions (IMF). Correlation coefficients were used to distinguish correlative modes and uncorrelative modes, the dominant component of noise was denoised locally, and the information dimension(DI), fractal box dimension(DF) and weighted percolation entropy(WPE) were extracted from the reconstructed signals for feature fusion to form three-dimensional feature vectors. Finally, the SVM with optimized IALO parameters is used to identify RES. The simulation results show that the method can still get a high recognition rate under the condition of low signal-to-noise ratio(SNR), when the SNR is not less than 0dB, the recognition rate can reaches 100%, and the method has a strong anti-noise performance.
Understanding Gesture Performance for Object Selection in VR: Classification and Taxonomy of Gestures in HCI

Classification of body gestures is vital in HCI research as it benefits researchers with appropriate gestures considering user behavior, mental model, and constraints. This work presents the gesture classification of 87 unique gestures identified through an elicitation study for object selection in Virtual Reality (VR). In addition to adopting existing gesture taxonomies, we propose two new taxonomies based on hand dominance– (i) dominant hand only (ii) non-dominant hand first and (iii) equal and simultaneous hand dominance and multi-body part movement gestures – (i) simultaneous and (ii) sequential. We believe that the proposed classification and taxonomies will assist designers and researchers in designing effective full-body gestural interfaces, including VR interfaces.
Cross-lingual Knowledge Graph Alignment via Neighborhood Reconstruction Network

Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. Most of the previous cross-lingual knowledge graph alignment studies rely on entity information only from monolingual mapping information, which may fail at matching entities that have different structure information in two KGs. In this paper, we propose the Neighborhood Reconstruction Network (NRNet), a novel entity alignment framework for tackling the structural heterogeneity challenge. NRNet first expand their one-hop neighbors based on other KGs to reduce the difference between neighbor structures. Next, it calculates the mutual information between entities to capture both the topological structure and the neighborhood difference. We further used a solution based on graph attention to obtain graph-level matching vectors. Experiments on the benchmark dataset show that the effectiveness of NRNet by detailed ablation studies and analysis.
Cloud Computing Data Security Techniques—A Survey

From past few years cloud computing has become an IT slang. It is the grassland of computing that is spreading briskly day-by-day in scholastic and business in form to accomplish conditions of final users. Cloud computing in real sense is achieving any duty by building use of assistance that are granted by cloud providers. It empowers an ample dimension of customer to access Shared, Ascendable, and Virtualized resources on top of the World Wide Web. It is a chunk of shared computing. It is currently arriving grassland by dint of its act, towering opportunity, and small price. With the brisk rise of internet computing machinery there is a big requirement for data storage security on cloud. There endure many cloud data storage security techniques now a days. In this paper, we are declaring some of these current techniques, literature review, and comparison between them. From the given comparison table of various current cloud computing data security approaches, it is concluded that the best approach for cloud data storage security must be taken and considered. We believe that there exist many more approaches for cloud data storage security and among them the best approach must be chosen.
Application of Audio Processing Based on Image Semantic Segmentation in Applied Mathematics Online Course

This paper uses multimedia, network technology and computer-aided software as the core information technology, and audio processing technology based on image semantic segmentation. It analyzes the application of applied mathematics network courses, explores the connotation, characteristics, strategies and information resources of the construction of engineering mathematics network courses, and builds the online course teaching platform for students understands the core value of mathematics education and teaching, develops inquiry-based collaborative learning, and forms a personalized learning atmosphere. It has a unique function. It is a good environment for students to learn independently and lifelong learning, and improve the effect of after-class review to 6.5%.
RWRel: A fast training framework for random walk-based knowledge graph embedding

With the development of typical graph data scenarios such as social networks and recommender systems, the size of knowledge graphs is growing. In recent years, large-scale knowledge graphs have posed a challenge in terms of fast training of knowledge graph representation model when applied. The article proposes a fast training framework for random walk-based embedding of knowledge graphs (RWRel), which contains a random walk strategy based on relational paths and a relational encoding that introduces subject-object embeddings to model the subject-object semantics of relations in knowledge graphs. The experimental results show that the RWRel framework is effective in improving the speed and maintaining the performance of the representation learning method.
The Bitcoin protocol as a system of power

In this study, I use the Critical Realism perspective of power to explain how the Bitcoin protocol operates as a system of power. I trace the ideological underpinnings of the protocol in the Cypherpunk movement to consider how notions of power shaped the protocol. The protocol by design encompasses structures, namely Proof of Work and Trustlessness that reproduce asymmetrical constraints on the entities that comprise it. These constraining structures generate constraining mechanisms, those of cost effectiveness and deanonymisation, which further restrict participating entities’ ‘power to act’, reinforcing others’ ‘power over’ them. In doing so, I illustrate that the Bitcoin protocol, rather than decentralising and distributing power across a network of numerous anonymous, trustless peers, it has instead shifted it, from the traditional actors (e.g., state, regulators) to newly emergent ones.
An Efficient Water Strider Algorithm with Auto Encoder for Cervical Cancer Diagnosis using Pap Smear Images

Cervical cancer is considered a major cause of death from cancer among women. The severity of cervical cancer can be avoided by the earlier identification of the diseases. Medical image processing and intelligent models acts a vital part in the investigation of cancerous cells. Due to the recent advancements of several technologies, computer aided diagnosis (CAD) models becomes inexpensive and consume less time. Owing to the tremendous success of deep learning (DL) models in several medical fields, this paper presents an efficient water strider algorithm with autoencoder for cervical cancer diagnosis (WSAAE-CCD) technique using pap smear images. The goal of the WS AAE-CCD technique is for detecting the existence of cervical cancer using pap smear images. The WSAAE-CCD technique contains adaptive filtering (AF) based pre-processing to eradicate the existence of noise present in it. Besides, kernelized fuzzy C-means (KFCM) based segmentation approach was derived for determining the affected cervical cells and Residual Network (ResNet5) model is applied as a feature extractor. Finally, AE based classification model is employed and the optimal parameter tuning of the AE model takes place using the WSA. For inspecting the improved cervical cancer classification performance of the WS AAE-CCD technique, a detailed experimental analysis is made and the outcomes pointed out the betterment of the WSAAE-CCD technique.
3D Reconstruction of Spatial Non Cooperative Target Based on Improved Traditional Algorithm

A new 3D reconstruction strategy is proposed. The strategy obtains the camera internal reference matrix with known camera focal length and field of view angle; Improves the SURF algorithm to add the color information of feature points for feature point extraction and matching; Then takes the imaging time order of the image as a priori information, and solves the 3D sparse point cloud structure of camera position and non-cooperative target by SFM (structure from motion); Finally, CMVS (clustering multi-view stereo) is used to cluster the images., and PMVS (patch-based multi-view stereo) is used to generate the dense 3D point cloud structure by matching, expanding and filtering. The experimental results show that the improved 3D reconstruction strategy is effective to avoid the reconstruction errors caused by spatially non-cooperative targets due to no background texture, structural symmetry, and texture duplication.
RESHAPE: Reverse-Edited Synthetic Hypotheses for Automatic Post-Editing

Synthetic training data has been extensively used to train Automatic Post-Editing (APE) models in many recent studies because the quantity of human-created data has been considered insufficient. However, the most widely used synthetic APE dataset, eSCAPE, overlooks respecting the minimal editing property of genuine data, and this defect may have been a limiting factor for the performance of APE models. This article suggests adapting back-translation to APE to constrain edit distance, while using stochastic sampling in decoding to maintain the diversity of outputs, to create a new synthetic APE dataset, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RESHAPE</b> . Our experiments show that (1) RESHAPE contains more samples resembling genuine APE data than eSCAPE does, and (2) using RESHAPE as new training data improves APE models’ performance substantially over using eSCAPE.
A Discriminative Deep Neural Network for Text Classification

Text classification plays an important role in natural language processing. It has been widely applied to sentiment analysis, stance detection and fake news detection. Although previous work on text classification has made great progress in recent years, these methods do not consider discriminative. And the ignoration degrades the performance of text classification. Hence, this paper proposes a novel CNN-based method, which incorporates the power of discriminative by adding an extra regularization term. We conduct experiments on three datasets, and the results demonstrate that our model is superior to other popular text classification methods.
Sentiment Analysis of MicroBlog Comments Based on Multi-feature Fusion

The traditional word vector model based on context cannot effectively extract emotional features from multi-category online comment texts with short length, casual grammar and a large number of emojis. Aiming at the above, a multi-feature fusion model (WOOSD-CNN) integrating word order, semantics and dictionary features was proposed. Firstly, word order features obtained from Fasttext training were fused with semantic features obtained from Word2vec training. Then, according to the characteristics of emojis, the higher the frequency of emojis appearing in comments, the Universal Frequency of Emojis (EM-TDF) was introduced, and the emotional similarity matrix (ESV) is constructed by using it to obtain dictionary features. Finally, feature vectors were spliced of fusion and applied to emotion classification. Experiments were carried out on two datasets with multi-category MicroBlog comment. The experimental results show that the proposed model can obtain relatively accurate sentiment word vector, compared with Word2vec model, the indicators of Acc, Macro-F1 and Weighted-F1 have the highest improvement of 7.62%, 6.25% and 8.00%, respectively.
Ableism versus free speech in Australia: challenging online hate speech toward people with Down syndrome

Negative, ableist discourse challenging the rights of people with Down syndrome to receive support services and even to be born regularly arise on social media. This disability community has retaliated against such hate speech with resistance narratives that promote the value of people with Down syndrome, with few other recourses available. This article explores online free speech versus ableism in Australia through a major case study of 60 Minutes Australia Facebook discussions. Analysis identifies types of ableism expressed, types of harm caused and dimensions of grassroots responses. Using Timothy Garton Ash’s 2016 Ash, Garton, and Timothy. 2016. Free Speech: Ten Principles for a Connected World. New Haven: Yale University Press. https://ebookcentral.proquest.com/lib/rmit/detail.action?pq-origsite=primo&docID=4518753. [Google Scholar] work Free Speech as a basis for contemporary analysis, we discuss: to what extent should harmful and offensive comments directed at vulnerable populations be tolerated in the name of free speech; might negative, ableist discourse contribute to any social good; and what mechanisms beyond grassroots resistance narratives might effectively respond to harmful, online ableist discourses. Points of interestThe article looks at whether the right to free speech is more important than the harmful effects of online hate speech towards people with Down syndrome.We use the ideas in a book on Free Speech by Timothy Garton Ash to think about whether online hate speech should be allowed or silenced.Our analysis is based on a case study of Facebook discussion threads from a 60 Minutes Australia television segment.People in the disability community speaking up to create resistance narratives is the main way that online disability hate speech is challenged.Other ways that could help are laws, government agencies providing support materials, and social media rules.
Distributed Ledger Framework for an Adaptive University Management System

The true power and the state of any technology are best known during difficult times. The yet-to-over COVID-19 lockdown has unleashed the power of online and remote modes of learning, evaluation, and working. The universities and educational institutions worldwide faced their big-time challenge in delivering, managing, evaluating their student’s learning progress. The need for an online-virtual-remote learning platform has become inevitable, which comes with a multitude of challenges, including usability, security, privacy, and real-time adoption. In this paper, we propose an Adaptive University Management System Framework (UMS) based on Distributed Ledger Technology (DLT) like Ethereum Blockchain where we record the schedules, the delivered classes, the materials and track the student’s learning progress with the use of ERC-721 standard to tokenize the submissions & evaluations. To provide efficient and effective utilization, the learning materials and submissions made onto the system are stored in Distributed File Storage system - InterPlanetary File System (IPFS) and hosted in InterPlanetary Name System (IPNS) with files securely split using a secret sharing algorithm.
DLPNet: Dynamic Loss Parameter Network using Reinforcement Learning for Aerial Imagery Detection

Object Detection on aerial imagery is a challenging task due to the following unique characteristics of the aerial imagery data: the large image size and the massive volume of data. Since the original image size from remote sensing sources is generally more colossal than the typical natural images, training detection models with a small mini-batch size are expected for the many practical, real-world applications. Furthermore, using a small mini-batch size enable the model to have better generalization performance. However, it causes an unstable learning process due to high gradient noise. In this case, reducing the learning rate enable the model to train reliably. However, a small learning rate can cause slow learning and convergence to the local minimum point also. Therefore, in this work, we propose a novel method, DLPNet, to enable robust and stable training with a small mini-batch size and various learning rates. Our model composes of an object detection model and a reinforcement learning agent. Our reinforcement learning agent extracts features from training mini-batch data and determines optimal parameters to the loss function of the object detection model. This dynamic loss function with adaptive parameters can achieve a more robust and stable learning process than the original baseline model. We demonstrate the effectiveness of our approach with a challenging object detection dataset, DOTA-v2.0. In addition, we release our code for reproducibility and to promote further research in this area1.