Reviewerly/exHarmony huggingface dataset:
User Performance in Virtual Reality Environments: The Capability of Immersive Virtual Reality Systems in Enhancing User Spatial Awareness and Producing Consistent Design Results

Spatial decision-making in different virtual environments may vary based on the levels of spatial awareness experienced within Virtual Reality (VR) systems. Features and characteristics of VR systems may act as facilitators or distractors of spatial awareness, which can result in extreme variations in user spatial decisions. This research explored the capability of an Immersive Virtual Reality Interactive Environment (IVRIE) and a desktop-based VR (DT system) in reducing extreme variations in spatial decisions and inconsistent design results. Users’ spatial decisions, performance, and design results in both systems were studied regarding the impact of these two systems’ features on users, including the sense of immersion, types of interaction, and usage of eye-level view in spatial designs. The design results produced in both systems were compared considering the types of enclosure, surface texture, scale, and spatial function. Descriptive and inferential statistical comparisons and testing using quantitative and qualitative data were applied to determine participants’ performance regarding the production of spatial outliers in each system. The results showed that IVRIE was more effective than the DT system fostering a consistency of space sizes and reducing outliers.
The Systems Approach and Design Path of Electronic Bidding Systems Based on Blockchain Technology

The electronic tendering and bidding system has realized the digitalization, networking, and high integration of the whole process of tendering, bidding, bid evaluation, and contract, which has a wide range of applications. However, the trust degree, cooperation, and transaction efficiency of the parties involved in electronic bidding are low, and bidding fraud and collusion are forbidden repeatedly. Blockchain technology has the characteristics of decentralization, transparent transactions, traceability, non-tampering and forgery detection, and data security. This paper proposes a design path of an electronic bidding system based on blockchain technology, which aims to solve the efficiency, trust, and security of the electronic trading process. By building the underlying architecture platform of blockchain and embedding the business process of electronic bidding, this realizes the transparency, openness, and traceability during the whole process of electronic bidding. This paper uses qualitative and quantitative methods to prove the effectiveness of the system.
Research on the Effectiveness of Deep Convolutional Neural Network for Electromagnetic Interference Identification Based on I/Q Data

With the development of wireless communication technology, the electromagnetic interference (EMI) of artificial radio to weather radar increases significantly, which has a serious impact on the quality of radar data. Most of the research on detecting and suppressing electromagnetic interference was based on the primary product of the radar. This paper researches the effectiveness of deep convolutional neural networks (DCNN) to identify and suppress electromagnetic interference based on the I/Q data output from the front end of a radar receiver. Firstly, this paper selected UNet, ResNet with UNet structure, and DeepLab V3+ for the semantic segmentation of electromagnetic interference and other signals. After semantic segmentation, this paper used the linear interpolation method to suppress EMI. Finally, this paper selected the prediction precision of the model and compared the quality of primary products before and after EMI suppression to evaluate the effectiveness of DCNN. The results showed that all three models could effectively identify the electromagnetic interference and the quality of the data were improved after suppression. It suggests that the use of DCNN on the I/Q data output from the front end of a radar receiver can play a certain effect on the identification of electromagnetic interference.
Optimal deep transfer learning based ethnicity recognition on face images

In recent times, deep learning driven face image analysis has gained significant interest among several application areas like surveillance, security, biometrics, etc. The facial analysis intends to compute facial soft biometrics like ethnicity, expression, identification, age, gender, and so on. Among several biometrics, ethnicity recognition remains a hot research area. Recent advancements in computer vision (CV) and artificial intelligence (AI) models form the basis of an effective design of ethnicity recognition models. With this motivation, this paper introduces a novel Harris Hawks optimization with deep transfer learning based fusion model for face ethnicity recognition (HHODTLF-FER) model. The proposed HHODTLF-FER model is to determine the different kinds of ethnicity for applied facial images. A fusion of three pre-trained DL models, namely VGG16, Inception v3, and capsule networks (CapsNet) models, are employed. In addition, bidirectional long short term memory (BiLSTM) model is applied for ethnicity recognition and Classification. Finally, HHO algorithm is utilized to fine tune the hyperparameters contained in the BiLSTM model, showing the novelty of the work. In order to ensure the improved recognition performance of the HHODTLF-FER model, a wide ranging experimental analysis is performed using benchmark databases. The comprehensive comparative study highlighted the promising performance of the HHODTLF-FER model over the other approaches. • To develop an ethnicity recognition on face images using harris hawks optimization basd deep transfer learning. • To improve the classification performance, bidirectional long short term memory (BiLSTM) model is applied for ethnicity recognition and Classification. • To improve the classification performance, HHO algorithm is utilized to fine tune the hyperparameters contained in the BiLSTM model, showing the novelty of the work.
A validation study on the factors affecting the practice modes of open peer review

Abstract In this paper we conduct a validation study on the factors affecting the practice modes of open peer review. Taking the Open Access Journals (OAJ) in Directory of Open Access Journals (DOAJ) as the research objects, we crawled the internet to gather their relevant data. Based on the method of categorical variable assignment, a quantitative analysis was performed on the qualitative factors that affect the practice modes of open peer review. A multi-dimensional analysis chart is used to illustrate the relationships between the factors. Optimal scale regression modeling and discriminant analysis were also employed to reveal the degrees of influences by the factors. The public categories of “type of open peer review” and “reviewer identity” are closely related to each other. “Reviewer identity” has evident positive influence on “type of open peer review”, and the degree of influence is the highest. Therefore, “reviewer identity” is the primary and most crucial factor affecting open peer review practice modes. “Review report” and “order of review report and publication” are the secondary ones. Whether or not the identities of review experts are open has become the most important factor affecting the practice modes of open peer review. Transparent peer review is currently the most effective practice mode of open peer review. Technologies like block chain can be used to address the psychological uneasiness for the peer review experts who are concerned with privacy issues. The fact that most OAJs use “pre-publication review” shows that open peer review still plays the traditional role of “academic goalkeeper”. Publication of peer review reports actually helps peer review experts augment their reputation, which in turn practically promotes the development of open peer review.
CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification

Relation classification is an important fundamental task in information extraction, and convolutional neural networks have been commonly applied to relation classification with good results. In recent years, due to the proposed pre-training model BERT, the use of which as a feature extraction architecture has become more and more popular, convolutional neural networks have gradually withdrawn from the stage of NLP, and the relation classification/extraction model based on pre-training BERT has achieved state-of-the-art results. However, none of these methods consider how to accurately capture the semantic features of the relationships between entities to reduce the number of noisy words in a sentence that are not helpful for relation classification. Moreover, these methods do not have a systematic prediction structure to fully utilize the extracted features for the relational classification task. To address these problems, a SpanBert-based relation classification model is proposed in this paper. Compared with existing Bert-based architectures, the model is able to understand the semantic information of the relationships between entities more accurately, and it can fully utilize the extracted features to represent the degree of dependency of a pair of entities with each type of relationship. In this paper, we design a feature fusion method called “SRS” (Strengthen Relational Semantics) and an attention-based prediction structure. Compared with existing methods, the feature fusion method proposed in this paper can reduce the noise interference of irrelevant words when extracting relational semantics, and the prediction structure proposed in this paper can make full use of semantic features for relational classification. We achieved advanced results on the SemEval-2010 Task 8 and the KBP37 relational dataset.
Neural graph collaborative filtering for privacy preservation based on federated transfer learning

Purpose In recent years, personalized recommendations have facilitated easy access to users' personal information and historical interactions, thereby improving recommendation effectiveness. However, due to privacy risk concerns, it is essential to balance the accuracy of personalized recommendations with privacy protection. Accordingly, this paper aims to propose a neural graph collaborative filtering personalized recommendation framework based on federated transfer learning (FTL-NGCF), which achieves high-quality personalized recommendations with privacy protection. Design/methodology/approach FTL-NGCF uses a third-party server to coordinate local users to train the graph neural networks (GNN) model. Each user client integrates user–item interactions into the embedding and uploads the model parameters to a server. To prevent attacks during communication and thus promote privacy preservation, the authors introduce homomorphic encryption to ensure secure model aggregation between clients and the server. Findings Experiments on three real data sets (Gowalla, Yelp2018, Amazon-Book) show that FTL-NGCF improves the recommendation performance in terms of recall and NDCG, based on the increased consideration of privacy protection relative to original federated learning methods. Originality/value To the best of the authors’ knowledge, no previous research has considered federated transfer learning framework for GNN-based recommendation. It can be extended to other recommended applications while maintaining privacy protection.
A service lens on cybersecurity continuity and management for organizations’ subsistence and growth

Purpose This paper proposes a holistic, proactive and adaptive approach to cybersecurity from a service lens, given the continuously evolving cyber-attack techniques, threat and vulnerability landscape that often overshadow existing cybersecurity approaches. Design/methodology/approach Through an extensive literature review of relevant concepts and analysis of existing cybersecurity frameworks, standards and best practices, a logical argument is made to produce a dynamic end-to-end cybersecurity service system model. Findings Cyberspace has provided great value for businesses and individuals. The COVID-19 pandemic has significantly motivated the move to cyberspace by organizations. However, the extension to cyberspace comes with additional risks as traditional protection techniques are insufficient and isolated, generally focused on an organization's perimeter with little attention to what is out there. More so, cyberattacks continue to grow in complexity creating overwhelming consequences. Existing cybersecurity approaches and best practices are limited in scope, and implementation strategies, differing in strength and focus, at different levels of granularity. Nevertheless, the need for a proactive, adaptive and responsive cybersecurity solution is recognized. Originality/value This paper presents a model that promises proactive, adaptive and responsive end-to-end cybersecurity. The proposed cybersecurity continuity and management model premised on a service system, leveraging on lessons learned from existing solutions, takes a holistic analytical view of service activities from source (service provider) to destination (Customer) to ensure end-to-end security, whether internally (within an organization) or externally.
The Trends and Challenges of Virtual Technology Usage in Western Balkan Educational Institutions

Higher educational institutions in Western Balkan countries strive for continuous development of their teaching and learning processes. One of the priorities is employing state-of-the-art technology to facilitate experience-based learning, and virtual and augmented reality are two of the most effective solutions to providing the opportunity to practice the acquired theoretical knowledge. This report presents (apart from the theoretical introduction to the issue) an overall picture of the knowledge of AR and VR technology in education in Western Balkan universities. It is based on a semi-structured online questionnaire whose recipients were academic staff and students from universities in Albania, Kosovo, and North Macedonia. The questionnaire differed for each target group; the version for academics comprised 11 questions for 710 respondents, and the version for students comprised 10 questions for 2217 respondents. This paper presents and discusses the results for each question with the aim to illustrate Western Balkan countries’ current state of VR and AR application in education.
Robustness in bio‐inspired visually guided multi‐agent flight and the gain modulation hypothesis

Abstract Aerial swarm implementations often underperform their feedback design goals due to sensor noise, modeling errors, and external disturbances, each of which can often be mitigated by including robustness in the design approach. At the same time, gain modulation has been measured in numerous insect flight examples involving relative position regulation and tracking. Gains are often lower than what initially appears optimal, which is not fully explained by existing hypotheses. This article analyzes the robustness of a visual feedback control system modeling insect flight feedback paths by using optic flow to regulate an agent's motion relative to its neighbors. This study considers the system's robustness to sensor noise and configuration deviations, which appear as a nonlinearity in the observation equation often neglected in linear feedback. The analysis indicates robust performance can be attained by a static gain controller by appropriate choice of gain. Experimental results using an optic flow swarm implementation in robotic systems are used to quantify the actual system performance for static gain controllers. Performance is compared when the system responds to ideal optic flow (no sensor noise) and to camera‐based optic flow swarm implementation (sensor noise and configuration uncertainty). [Correction added on 10 November 2022, after first online publication: the underline in the word ‘compared’ of the preceding sentence has been removed in this version.] The analysis suggests neural gain modulation in insect feedback paths may support robust group motion stabilization.
DEVELOPING COMMUNICATION COMPETENCE IN DIGITAL LEARNING ENVIRONMENTS FOR PRIMARY SCIENCE STUDENTS

Effective natural science teaching in primary schools helps students to form and change their conceptions about nature and natural processes. This is only possible through communicating about learners’ existing misconceptions and the process of transforming them into correct scientific concepts – using a socio-constructivist learning approach. The aim of this study was to find out what happens when the natural science teaching/learning process moves into digital learning environments. Immediately after the end of the Covid-induced school lock down, a sample of 183 first-, second- and third-grade teachers in Slovenia were asked to report about what happened in their science classes during the 11-week school lock down. The results reveal problems originating in very limited digital literacy competences as well as a lack of basic skills (in the first grade), and slowly emerging basic literacy in grades two and three. This had a great impact on the communication between teachers and students and between students themselves. According to research results, the problem significantly affects science teaching and is particularly urgent in didactics of chemistry and physics. Keywords: basic literacy, communication competence, digital literacy, primary school, science class, socio-constructivist learning approach
Evolving multi-user fuzzy classifier system with advanced explainability and interpretability aspects

Evolving classifiers and especially evolving fuzzy classifiers have been established as a prominent technique for addressing the recent demands in building classifiers in an incremental online manner, based on target labels typically provided by a single user. We present a framework for an interactive evolving multi-user fuzzy classifier system with advanced explainability and interpretability aspects (EFCS-MU-AEI). Multiple users may provide their label feedback based on which own users’ classifiers are incrementally trained with evolving learning concepts. Its classification outputs are amalgamated by a specific ensembling scheme, respecting (i.) uncertainty in the class labels due to labeling ambiguities among the users and (ii.) different experience levels of the users as voting weights. A major focus thereby is concentrated on the explainability of classification outputs for the purpose to increase the quality (consistency and certainty) of the user (labeling) feedbacks. It is established to show reasons why certain decisions have been made and with which certainty levels and rule coverage degrees. The reasons are deduced from the most active rules, which are reduced in their length by a statistically-motivated instance-based feature importance level concept. Another major focus lies on the interpretability of extracted rules in order to represent understandable knowledge contained in the classification problem and especially to realize the labeling behaviors of different users for different parts of the feature space (= different sample groups). A specific incremental feature weighting technique, respecting label uncertainties from multiple users and sample forgetting weights (for handling drifts), as well as a fuzzy set merging process are proposed to aim for a high compactness and transparency of the rules. Our approach was evaluated based on a visual inspection scenario. It could be shown that the explanations of the classifier decisions in fact significantly improved the labeling behavior of three single users in terms of showing higher accumulated accuracy trends. Feature weights integration into the classifier updates could achieve transparent rules with final essential four features to describe the classification problem. Based on this description, it turned out in which ways, i.e. for which sample groups, the users with lower experience levels should be taught to improve their understanding about the process.
Multimedia source identification using an improved weight photo response non-uniformity noise extraction model in short compressed videos

Source detection of digital images and videos is essential to multimedia security forensics research. Due to the defects of the manufacturing process, the optical sensor generates a photo response non-uniformity noise attached to it when generating multimedia content. This noise is widely regarded as the inherent fingerprint of multimedia due to its uniqueness and can be used for multimedia source detection. However, videos shared on social networking sites will be further compressed. It is difficult for existing methods to extract enough PRNU noise from such compressed videos for source identification, especially short compressed videos. Considering the effect of compression on PRNU noise, a weighted PRNU extraction model based on variance-stabilized transformed multi-scale iterative least-squares filtering is proposed. First, the video's decoder needs to be reconstructed. When the decoder's loop filter is removed, more PRNU noise is retained in the video frame. Secondly, a multi-scale iterative least-squares filtering algorithm based on the variance stabilized transform (VST) is proposed, which can extract more PRNU noise from the video frames. Finally, maximum likelihood estimation using quantization parameter (QP) weighting enables a better estimate of the PRNU and suppression of other noise. Experiments on the public VISION database show that the proposed PRNU extraction model improves the recognition performance by about 20% on average over existing models.
A centralized contact-tracing protocol for the COVID-19 pandemic

Digital contact tracing (DCT) is one of the weapons to be used against the COVID-19 pandemic, especially in a post-lockdown phase, to prevent or block foci of infection. As DCT systems can handle highly private information about people, great care must be taken to prevent misuse of the system and actions detrimental to people's privacy, up to mass surveillance. This paper presents a new centralized DCT protocol, called ZE2-P3T (Zero Ephemeral Exchanging Privacy-Preserving Proximity Protocol), which relies on smartphone localization but does not give any information about the user's location and identity to the server. Importantly, the fact that no exchange of ephemeral identities among users is required is the basis of the strong security of the protocol, which is proven to be more secure than the state-of-the-art protocol DP-3T/GAEN.
Graph-based query reformulation system for descriptive queries of jargon words using definitions

In information retrieval, the lexical mismatch is a common problem where texts in documents and user queries have different forms even though they have the same meaning. The problem occurs more frequently when people search for jargon words because they tend to use descriptive queries and the jargon words interchangeably such as "hypertension" and "high blood pressure". Query reformulation (QR) is one of the techniques that overcome the lexical gaps by transforming users' descriptive queries into appropriate jargon queries. This study proposes a novel graph-based QR system that uses a dictionary, where the model does not require manually annotated data. Given a descriptive query, our system predicts the corresponding jargon word over a graph consisting of headwords and words in their definition. We use a graph neural network to represent the relational properties between words and the curriculum learning to train the networks stably and gradually adjust to hard samples. Moreover, we propose a graph search model that finds the target node in real-time using the relevance scores of neighborhood nodes. By adding this fast graph search model to the front of the proposed system, we reduce the reformulating time significantly. Experimental results on two datasets show that the proposed method can effectively reformulate descriptive queries to corresponding jargon words as well as improve retrieval performance under several search frameworks.