Reviewerly/exHarmony huggingface dataset:
Are Students Ready to Be Immersed? Acceptance of Mobile Immersive Virtual Reality by Secondary Education Students

As immersive virtual reality (iVR) is gaining popularity, interest from education and educational research is growing likewise. As such, it is of interest to investigate which factors thrive and inhibit acceptance of this iVR technology in education. In this study we investigated the perceptions of 2,640 Flemish secondary education students on iVR as an instructional tool. As a theoretical framework, we adopted the UTAUT2 model extended with the factor of personal innovativeness in the domain of information technology. Students watched a video with several examples of iVR educational experiences. Next, their perceptions on iVR as an instructional method were measured using an online survey. To test the several hypotheses, we applied general linear modelling. The results account for 50% of variance in behavioral intention to use. These findings help to understand which factors contribute to the acceptance of iVR in secondary education and might guide the design of evidence-informed implementation plans by school managers and teachers.
The Role of Context and Interaction When Learning With Augmented 360° Photos

360° photos can be used to place learners into different environments augmented with additional information in the form of virtual overlays. This way, more information can be provided than usually, resembling context-based augmented reality (AR). Both contextuality and interactivity are important aspects when it comes to AR- and 360° photo-based learning environments. To find out more about the specific impact context and interaction with the learning material have in an augmented 360° photo environment, we conducted an experimental 2 × 2 between-subjects design with the factors context (visible vs. non visible) and interaction (learner vs. system control) with N = 138 participants. We examined variables concerning immersion, motivation and learning outcome. Concerning immersion, we found a large positive main effect of learner control. Concerning motivation, we found positive main effects of context visibility and learner control. For a subfactor of motivation, satisfaction, we found an interaction effect showing a disadvantage of non-visible context and system control in comparison to all other conditions. We found no effects on learning outcome. We discuss the limitations and implications of the study considering the theoretical background.
Framework of Pedagogic and Usability Principles for Effective Multi-user VR Learning Applications

Education and learning continue to evolve as a result of the rapid speed of technological advancement in the 21st Century. The growing presence of emerging technologies increases the potential of an innovative environment that can promote and facilitate learning. The impact of immersive technology in the field of learning has been widely discussed and studied by the scientific community in the first two decades of the 21st Century. This is especially true with the advent of the ambition for the unified and fluid multi-user virtual world - Metaverse and the limiting realities of the COVID-19 pandemic - where new modes of virtual interaction are in the spotlight, including the field of education. The success of VR educational content, especially multi-user educational content, depends, in large part, on how usable and adaptable it is for learning purposes. This study investigates pedagogic and usability principles for effective multi-user VR learning applications based on the Technology Enhanced Learning approach, Gagne’s Nine Events of Instruction model and Nielsen’s usability heuristics and analyses survey data collected during multi-user VR content testing. The study used a mixed-methods approach involving multi-user VR environment testing with a group of Higher Education students. This study tested three different VR multi-user learning applications to evaluate them from various pedagogical and usability perspectives and collected survey data to gain a deeper understanding of the students’ experience. This study proposes a framework of pedagogic and usability principles for developing effective multi-user VR learning applications.
STEAM Project Exhibition in the Metaverse for Deaf High School Students’ Affective Empowerment: The Power of Student Museum Exhibitions in Social Virtual Reality

The Metaverse is the three-dimensional iteration of the Internet, a perpetual open web of persistent, networked environments merging physical reality with digital virtuality. Science Technology Engineering Art Mathematics (STEAM) education bridges two knowledge domains often perceived as disjointed: science and technology with art, humanities and social studies. This paper presents the design and development of a multi-school transdisciplinary STEAM project orchestrated by the University of Patras Science and Technology Museum. The deliverables of the project were analog and digital artifacts produced by K-12 primary and secondary school students on digital literacy and future citizenship. These were presented in an innovative virtual reality exhibition that was open and accessible in a web-based 3D online environment. The study employs an exploratory case study design involving deaf high school students and teachers. Data was collected from observation and semi-structured interviews. Results showed that the exhibition of deaf student creations in a multiuser platform in the Metaverse produced a series of social ripple effects around the students themselves, school peers, with and without hearing difficulties as well as educators. Its main contribution is the practical demonstration of the social affordances of the Metaverse in educational projects for children and adolescents with hearing disabilities.
Pre-clustered Generative Adversarial Network Model for Mongolian Font Style Transfer

Font style transfer has important application value in the field of data enhancement and can be used to alleviate the problem of insufficient data in fields such as handwritten character recognition, glyph inference restoration, and ancient book restoration. The complexity of traditional Mongolian has brought many challenges to character recognition and restoration of ancient books. This paper first builds a small-scale Mongolian font style dataset and a graph cluster aggregator algorithm. Secondly, an improved conditional generative adversarial neural network model with MSE loss function is proposed, and the self-built dataset is used to train the model after image aggregation. The experimental results show that the model can learn the traditional Mongolian font style and transfer it to the same semantic text in a small amount of training, and generate images with prominent style.
Dead or Alive: Continuous Data Profiling for Interactive Data Science

Profiling data by plotting distributions and analyzing summary statistics is a critical step throughout data analysis. Currently, this process is manual and tedious since analysts must write extra code to examine their data after every transformation. This inefficiency may lead to data scientists profiling their data infrequently, rather than after each transformation, making it easy for them to miss important errors or insights. We propose continuous data profiling as a process that allows analysts to immediately see interactive visual summaries of their data throughout their data analysis to facilitate fast and thorough analysis. Our system, AutoProfiler, presents three ways to support continuous data profiling: (1) it automatically displays data distributions and summary statistics to facilitate data comprehension; (2) it is live, so visualizations are always accessible and update automatically as the data updates; (3) it supports follow up analysis and documentation by authoring code for the user in the notebook. In a user study with 16 participants, we evaluate two versions of our system that integrate different levels of automation: both automatically show data profiles and facilitate code authoring, however, one version updates reactively (“live”) and the other updates only on demand “dead”). We find that both tools, dead or alive, facilitate insight discovery with 91% of user-generated insights originating from the tools rather than manual profiling code written by users. Participants found live updates intuitive and felt it helped them verify their transformations while those with on-demand profiles liked the ability to look at past visualizations. We also present a longitudinal case study on how AutoProfiler helped domain scientists find serendipitous insights about their data through automatic, live data profiles. Our results have implications for the design of future tools that offer automated data analysis support.
Depth Image Hashing Algorithm Based on Local Global Feature Fusion

In recent years, deep learning-based hashing techniques have garnered significant attention within the academic community, owing to their impressive performance achievements. However, the current deep hashing method is limited by its use of neural networks cannot make good use of the important feature information of the image, thus limiting the retrieval accuracy. In this paper, we design a two-channel feature extraction network called the local and global feature fusion network (LGDH). Specifically, the features extracted by visual geometry group (VGG) network and con-volution-enhanced image transformer (CeiT) network are deeply orthogonal fusion of local and global features, which solves the problem of lack of semantic correlation in existing deep hash methods. To fully harness the insights gained from previous hash function learning, we employ long short-term memory (LSTM) decoder to proficiently shape and create a resilient public binary code space. Through detailed experiments, our proposed method has achieved the Sota level in three public datasets CIFAR10, NUS-WIDE and MIRFLICKR compared with multiple cutting-edge algorithms, which can prove the effectiveness of our pro-posed method.
Affective Visualization Design: Leveraging the Emotional Impact of Data

In recent years, more and more researchers have reflected on the undervaluation of emotion in data visualization and highlighted the importance of considering human emotion in visualization design. Meanwhile, an increasing number of studies have been conducted to explore emotion-related factors. However, so far, this research area is still in its early stages and faces a set of challenges, such as the unclear definition of key concepts, the insufficient justification of why emotion is important in visualization design, and the lack of characterization of the design space of affective visualization design. To address these challenges, first, we conducted a literature review and identified three research lines that examined both emotion and data visualization. We clarified the differences between these research lines and kept 109 papers that studied or discussed how data visualization communicates and influences emotion. Then, we coded the 109 papers in terms of how they justified the legitimacy of considering emotion in visualization design (i.e., why emotion is important) and identified five argumentative perspectives. Based on these papers, we also identified 61 projects that practiced affective visualization design. We coded these design projects in three dimensions, including design fields (where), design tasks (what), and design methods (how), to explore the design space of affective visualization design.
Atmospheric Turbulence Degraded Video Restoration with Recurrent GAN (ATVR-GAN)

Atmospheric turbulence (AT) can change the path and direction of light during video capturing of a target in space due to the random motion of the turbulent medium, a phenomenon that is most noticeable when shooting videos at long ranges, resulting in severe video dynamic distortion and blur. To mitigate geometric distortion and reduce spatially and temporally varying blur, we propose a novel Atmospheric Turbulence Video Restoration Generative Adversarial Network (ATVR-GAN) with a specialized Recurrent Neural Network (RNN) generator, which is trained to predict the scene's turbulent optical flow (OF) field and utilizes a recurrent structure to catch both spatial and temporal dependencies. The new architecture is trained using a newly combined loss function that counts for the spatiotemporal distortions, specifically tailored to the AT problem. Our network was tested on synthetic and real imaging data and compared against leading algorithms in the field of AT mitigation and image restoration. The proposed method outperformed these methods for both synthetic and real data examined.
Port-Based Anonymous Communication Network: An Efficient and Secure Anonymous Communication Network

With the rise of the internet, there has been an increasing focus on user anonymity. Anonymous communication networks (ACNs) aim to protect the identity privacy of users in the network. As a typical ACN, Tor achieves user anonymity by relaying user data through a series of relay nodes. However, this results in higher latency due to the transmission of network traffic between multiple nodes. This paper proposes a port-based anonymous communication network (PBACN) to address this issue. First, we propose a path construction algorithm. This algorithm describes constructing paths by partitioning the communication path information, which can reduce the probability of being discovered by adversaries. Secondly, we design a port-based source routing addressing method. During data transmission from the source to the destination, each node can directly forward the data by resolving the address into the port of each node. This method eliminates the need for table lookups, reducing the complexity of routing. Lastly, we propose an entropy-based metric to measure the anonymity of different ACNs. In terms of experimental evaluation, we quantitatively analyze the anonymity and end-to-end delay of various ACNs. The experimental results show that our proposed method reduces end-to-end delay by approximately 25% compared to Tor. When the adversary fraction is 20%, PBACN can improve the anonymity degree by approximately 4%.
Experimental EMFI detection on a RISC-V core using the Trace Verifier solution

Physical attacks are powerful threats that can cause changes in the execution behavior of a program. Control-Flow Integrity (CFI) is used to check the program’s flow execution, ensuring that it remains unaltered by these attacks. The RISC-V Trace Encoder (TE) provides valuable information about the user program’s execution path, and is used as part of a CFI solution. An enhanced version of the TE specifications permits detecting intricate fault models such as the corruption of any discontinuity instruction, using an additional Trace Verifier (TV) hardware module. In this paper, we present a buffer overflow software attack simulation and experimental ElectroMagnetic Fault Injection (EMFI) attacks conducted on an Field Programmable Gate Array (FPGA) board that implements a RISC-V core linked to the enhanced TE and TV modules. Unlike existing CFI solutions, our proposed approach does not require modifications to the RISC-V compiler, user application code or the RISC-V core. The average overhead of our solution in terms of hardware area, memory and power consumption are equal to 13.6%, 3.5% and 9% respectively.
Data-Matching-Based Privacy-Preserving Statistics and Its Applications in Digital Publishing Industry

With the rapid development of digital media technology, many people prefer to read e-books over article versions. The digital publishing platform can collect and analyze massive amounts of readers’ reading information. The statistical analysis results can be regarded as the platform's digital assets based on which it provides paid services for its users. However, three privacy issues are related to readers’ reading information, users’ statistical preferences, and the platform's digital assets. This article proposes a data-matching-based privacy-preserving statistic scheme. The proposed solution combines bloom filters, secret sharing, and perturbing technologies to realize an efficient match between users’ statistical preferences and massive readers’ corresponding reading information and statistical analysis of the matching results without compromising the privacy of different parties. Besides, the proposed solution adopts an edge computing paradigm to realize the process of massive data in a divide-and-conquer parallel way. It introduces the concepts of Mirror Secret Shares and Buddy Edge Devices to virtualize the <inline-formula><tex-math notation="LaTeX">$(m+1, m+1)$</tex-math></inline-formula> -threshold secret sharing scheme to an <inline-formula><tex-math notation="LaTeX">$(m+1, m+\lfloor m/2 \rfloor +2)$</tex-math></inline-formula> -threshold secret sharing scheme for achieving good robustness without adding hardware devices. The detailed analyses show that our solution meets the defined design goals. Furthermore, the experimental results demonstrate the efficiency of the proposed work.
RecFRCN: Few-Shot Object Detection With Recalibrated Faster R-CNN

Currently, Faster R-CNN is used as the fundamental detection framework in the majority of few-shot object detection algorithms. However, the under-explored few-shot classification branch generates a huge number of low-quality scores, and this high-score false positive leads to poor classification task performance. In order to solve this problem, we propose a novel method, called Recalibrated Faster R-CNN, to recalibrate the categories of regression boxes. Specifically, we introduce a new classification network (Rec-Net) for Faster R-CNN’s Box Predictor, consisting of a feature extractor, a feature enhancement Block (FEB), an ROI Pooling layer, and the local descriptor classifier (LDC). The feature extractor extracts features from input images. FEB enhanced the features obtained by the feature extractor. The ROI Pooling layer projects each prediction box output from Faster R-CNN into the feature map and pools it into a feature map with a fixed size. LDC not only obtains the optimal depth local descriptor from each ROI features for the image-to-class measure, but also, in the case of the few-shot setting, since the adoption of local representation can be seen as a natural data enhancement, this measure can be more efficient and thus improve the original classification score. The experimental results show that our method gets good scores on several benchmark tests.
Usage of Online Platforms in Education of Mathematics in Transcarpathia at the Beginning of Quarantine

Distance learning and e-learning as concepts have been in our minds for a long time. In March 2020, they suddenly gained great importance due to the introduction of quarantine and were immediately put into practice. It had to be applied in the everyday lives of teachers and students with surprising speed. The goal of this research is to assess and demonstrate how teachers overcome the difficulties of mathematics education in distance learning. For this purpose, a month later after the beginning of distance education, I conducted a questionnaire survey among 20 teachers of mathematics in Transcarpathia who teach in several educational institutions with different work experiences. They were asked how education went on during quarantine, how they chose the platforms and methods needed to hold their lessons, what the checking and testing process was, what advantages and disadvantages they faced in distance learning.
RoBERTa-CoA: RoBERTa-Based Effective Finetuning Method Using Co-Attention

In the field of natural language processing, artificial intelligence (AI) technology has been utilized to solve various problems, such as text classification, similarity measurement, chatbots, machine translation, and machine reading comprehension. Significant advancements have been made in complex and rule-intensive natural language processing through deep learning, in which machines directly learn patterns. Machine reading comprehension, a natural language processing task, involves machines understanding questions and paragraphs to find answers within paragraphs. In 2019, bidirectional encoder representations from transformers (BERT) and a robust optimized BERT pretraining approach (RoBERTa) were introduced. They were then optimized for pretraining and fine-tuning, resulting in significant advancements. RoBERTa outperformed BERT in terms of training speed and performance by increasing the pretraining data and batch sizes, employing dynamic masking, and eliminating the next sentence prediction task. In RoBERTa, machine reading comprehension involves simultaneously inputting questions and paragraphs. However, this simultaneous input method suffers from the attention separate representation (ASP) problem, in which the attention distribution between the question and the paragraph spreads widely across keywords. This study proposed two methods to address the ASP problem. The existing input format, question-paragraph, was changed to three independent inputs: question, paragraph, and concatenated RoBERTa outputs. The concatenated matrix was then transformed into two matrices, and a machine reading comprehension algorithm using co-attention was proposed. An ablation study was conducted to evaluate and analyze the model’s performance, comprehension, and design efficiency. According to the experimental results, the proposed method improved the EM by 0.9% and F1 by 1.0% compared to the existing methods. Consequently, the learning performance was enhanced through attention concentration and co-attention, and it could demonstrate much better performance compared to the existing models.
Task-Free Fairness-Aware Bias Mitigation for Black-Box Deployed Models

With AI systems widely deployed in societal applications, the fairness of these models is of increasing concern, for instance, hiring systems should recommend applicants impartially from different demographic groups, and risk assessment systems must eliminate racial inequity in the criminal justice system. Therefore, ensuring fairness in these models is crucial. Most existing methods guarantee the fairness of AI systems by leveraging data augmentation to mitigate biases in the training set or introducing fairness principles into the training process. However, these methods can not be applied to black-box models that have been already deployed, as retraining and redeployment would be expensive. By contrast, we propose Task-Free Fairness-Aware Adversarial Perturbation (TF-FAAP), a flexible approach for improving the fairness of black-box deployed models by adding perturbations on input samples that blind their fairness-related attribute information without modifying the model's parameters or structures. Inspired by adversarial learning, the proposed TF-FAAP consists of a discriminator and a generator to create universal fairness-aware perturbations for a variety of tasks. The former aims to distinguish fairness-related attributes, and the latter generates perturbations to make the discriminator's prediction distribution of fairness-related attributes uniform. To preserve the utility of perturbed samples, we maximize the mutual information between their representations and corresponding original samples, retaining more original samples' information. One key advantage of our method is that it can be universally applied to black-box deployed models to improve their fairness, as the fairness-related attribute information is mixed/hidden and can not construct spurious associations with target labels. In addition, the perturbation generated by TF-FAAP has a high transferability, i.e., the perturbations learned on one dataset can also alleviate the unfairness of a model trained on a different dataset. The extensive experimental evaluation demonstrated the effectiveness and superior performance of our method.