Reviewerly/exHarmony huggingface dataset:
Comparing Heuristic Rules and Masked Language Models for Entity Alignment in the Literature Domain

The cultural world offers a staggering amount of rich and varied metadata on cultural heritage, accumulated by governmental, academic, and commercial players. However, the variety of involved institutions means that the data are stored in as many complex and often incompatible models and standards, which limits its availability and explorability by the greater public. The adoption of Linked Open Data technologies allows a strong interlinking of these various databases as well as external connections with existing knowledge bases. However, as they often contain references to the same entities, the delicate issue of entity alignment becomes the central challenge, especially in the absence or scarcity of unique global identifiers. To tackle this issue, we explored two approaches, one based on a set of heuristic rules and one based on masked language models, or masked language models (MLMs). We compare these two approaches, as well as different variations of MLMs, including some models trained on a different language, and various levels of data cleaning and labeling. Our results show that heuristics are a solid approach but also that MLM-based entity alignment obtains better performance coupled with the fact that it is robust to the data format and does not require any form of data preprocessing, which was not the case of the heuristic approach in our experiments.
Parameterizing Lognormal state space models using moment matching

Abstract In ecology, it is common for processes to be bounded based on physical constraints of the system. One common example is the positivity constraint, which applies to phenomena such as duration times, population sizes, and total stock of a system’s commodity. In this paper, we propose a novel method for parameterizing Lognormal state space models using an approach based on moment matching. Our method enforces the positivity constraint, allows for arbitrary mean evolution and variance structure, and has a closed-form Markov transition density which allows for more flexibility in fitting techniques. We discuss two existing Lognormal state space models and examine how they differ from the method presented here. We use 180 synthetic datasets to compare the forecasting performance under model misspecification and assess the estimation of precision parameters between our method and existing methods. We find that our models perform well under misspecification, and that fixing the observation variance both helps to improve estimation of the process variance and improves forecast performance. To test our method on a difficult problem, we compare the predictive performance of two Lognormal state space models in predicting the Leaf Area Index over a 151 day horizon by using a process-based ecosystem model to describe the temporal dynamics. We find that our moment matching model performs better than its competitor, and is better suited for intermediate predictive horizons. Overall, our study helps to inform practitioners about the importance of incorporating sensible dynamics when using models of complex systems to predict out-of-sample.
Embodied mixed reality with passive haptics in STEM education: randomized control study with chemistry titration

Researchers, educators, and multimedia designers need to better understand how mixing physical tangible objects with virtual experiences affects learning and science identity. In this novel study, a 3D-printed tangible that is an accurate facsimile of the sort of expensive glassware that chemists use in real laboratories is tethered to a laptop with a digitized lesson. Interactive educational content is increasingly being placed online, it is important to understand the educational boundary conditions associated with passive haptics and 3D-printed manipulables. Cost-effective printed objects would be particularly welcome in rural and low Socio-Economic (SES) classrooms. A Mixed Reality (MR) experience was created that used a physical 3D-printed haptic burette to control a computer-based chemistry titration experiment. This randomized control trial study with 136 college students had two conditions: 1) low-embodied control (using keyboard arrows), and 2) high-embodied experimental (physically turning a valve/stopcock on the 3D-printed burette). Although both groups displayed similar significant gains on the declarative knowledge test, deeper analyses revealed nuanced Aptitude by Treatment Interactions (ATIs). These interactions favored the high-embodied experimental group that used the MR device for both titration-specific posttest knowledge questions and for science efficacy and science identity. Those students with higher prior science knowledge displayed higher titration knowledge scores after using the experimental 3D-printed haptic device. A multi-modal linguistic and gesture analysis revealed that during recall the experimental participants used the stopcock-turning gesture significantly more often, and their recalls created a significantly different Epistemic Network Analysis (ENA). ENA is a type of 2D projection of the recall data, stronger connections were seen in the high embodied group mainly centering on the key hand-turning gesture. Instructors and designers should consider the multi-modal and multi-dimensional nature of the user interface, and how the addition of another sensory-based learning signal (haptics) might differentially affect lower prior knowledge students. One hypothesis is that haptically manipulating novel devices during learning may create more cognitive load. For low prior knowledge students, it may be advantageous for them to begin learning content on a more ubiquitous interface (e.g., keyboard) before moving them to more novel, multi-modal MR devices/interfaces.
Influence of Age, Gender and Years of Experience on Teachers in Promoting Strategies for Digital Sustainability and Data Protection

The aim of this research was to know how widespread the activities were, and to what extent they were being implemented, in relation to data protection and digital sustainability in Primary Education schools. This study also analyzed whether teachers’ age, gender and years of experience in the profession influenced the development of this type of practices among students. To this end, the GAUBIPRO (4150516/6) questionnaire, registered at the Spanish Patent and Trademark Office, was sent out to 308 Spanish teachers in Primary Education in Spain. The investigation was conducted by applying three multivariate statistical procedures: chi-squared with Kendall’s Tau-c coefficient, variance analysis using a single-factor ANOVA, and the HJ-Plot method, to find patterns of group behavior among the variables studied. The results showed that age, gender and years of experience were significant variables in the development of strategies for data protection and digital sustainability in Primary Education. For years of experience, the results showed that teachers with the highest mean number of years in the teaching profession were the least likely to apply data protection protocols. Male teachers were found to promote more general data protection strategies, while female teachers were more active in the design and use of activities in the classroom. In terms of age, the results clearly demonstrated that the higher the mean age of the teachers, the lower the rate of application of actions to promote data protection and digital security among young students, in three variables in particular: “foster strategies”, “advise students” and “inform families”.
Designing immersive stories with novice VR creators: a study of autobiographical VR storytelling during the COVID-19 pandemic

Virtual reality (VR) is increasingly being used as a tool for eliciting empathy and emotional identification in fact-based stories. However, it may not be clear whether VR stories authentically deliver the protagonists’ perspectives if the works are not created by or with the protagonists themselves. Therefore, it is crucial for the VR community to explore effective methods for democratizing VR storytelling, and to support novice VR designers in creating autobiographical stories. In this paper, we report findings from a collaborative design research project that aimed to create autobiographical stories with novice VR designers who lacked experience in VR storytelling. We collaborated with university students in Canada to design eight individual VR stories that expressed each student’s experiences of lockdown, during the early stages of the COVID-19 pandemic. We conducted interviews with the students to understand how VR contributed to conveying their individual experiences. Our findings demonstrate how immersive VR can be used as a meaningful tool for sharing autobiographical stories by delivering the character’s feelings, creating a sense of confinement and isolation, expressing inner worlds, and showing environmental details. Our discussion draws attention to the significance of careful camera positioning and movement in VR story design, the meaningful use of limited interaction and disorienting components, and the balance between spatial and temporal information in a three-dimensional environment. Our study highlights the potential of VR as an autobiographical storytelling tool and demonstrates how VR stories can be created through iterative collaboration between VR experts and novices.
Research on high school students’ behavior in art course within a virtual learning environment based on SVVR

Students who use spherical video-based virtual reality (SVVR) teaching materials for learning are able to gain more self-regulated, explorative, and immersive experiences in a virtual environment. Using SVVR teaching materials in art courses can present diverse and unique teaching effects, while also leading to the emergence of students' flow states.Therefore, through an art course teaching experiment, this study investigated 380 high school students and used structural equation modeling to analyze the antecedents and outcomes of students' flow state in using SVVR teaching materials.The results show that in using SVVR teaching materials in art courses, more attention should be paid to the control and telepresence in the antecedents of students' flow state.Only when they obtain better flow experiences can they have higher perceived usefulness and satisfaction with the content of the art course, as well as stronger intentions to continue using it. These results can provide a reference for the development and use of SVVR teaching materials in high school art courses.
Gamified dances, digital and socio-emotional skills in collaborative virtual environments of university students surviving the Covid-19 virus

The use of virtual dance avatars and virtual learning guides has enabled gamified dance teaching to virtualize the current university. In this experience, the objective was to test four hypotheses about the gamified dance developed in collaborative Zoom environments, and its effects on the digital and socioemotional skills of individuals who were severely or moderately infected by the Covid-19 virus between the years 2020 and 2022. We worked with 119 students and eight teachers from the Professional School of Primary Education of a private university in the city of Lima (Peru). An experimental design with pre and post-test was applied and Likert-type scales were used for data collection. The experience with gamified dances took place over four weeks in a dance competition at the university. The results allow us to assert that gamified dances developed digital skills, emotional skills, as well as socioemotional skills. These skills differ between the experimental group and the control group, with the exception of digital security skills, and the ability to improve self-esteem, which are skills that require more body practice as was achieved in the experimental group (dance virtual), which was also developed in the students of the control group (face-to-face dance).
Stabilized Performance Maximization for GAN-based Real-Time Authentication Image Generation over Internet

Abstract Providing the completely automated public test to tell computers and humans apart (CAPTCHA) services that are not vulnerable to learning is an important issue. Image-based CAPTCHA services have strengthened authentication by taking advantage of the fact that it is more difficult for computers to understand images, but the rapid growth of deep learning has made it possible to break and attack authentication. Image-based CAPTCHA uses pre-stored data, which enables learning and results in vulnerability. This paper presents an adaptive generative adversarial network (GAN) selection scheme using time-average image quality maximization subject to system/buffer stability. By using GAN, it can generate and provide new images for CAPTCHA authentication every time, preventing deep learning from learning images and enhancing security. In the image generation process, the trade-off exists between image quality and generation time, and in consideration of this trade-off, delay aware image-based authentication Lyapunov-based algorithm is proposed for stable and maximized performance. Moreover, through the performance evaluation, we investigate and show the existence of trade-off between generation time and generated image quality in the image generation process in both quantitative and qualitative manner.
Digital Teaching Competence According to the DigCompEdu Framework. Comparative Study in Different Latin American Universities

The relevance and importance that the topic of Digital Competences for Teachers (DCT) has gained is evident both in the field of training and in research, as can be seen by the increase in the amount of research and meta-analysis carried out on this topic. This article presents the results of an ex post facto research with a cross-sectional research design, based on a descriptive and hypothesis-testing approach. A total of 6,664 teachers from different Latin American universities participated in the self-knowledge they have regarding their Digital Teaching Competence (DTC) according to the DigCompEdu framework of the European Union. Among the results obtained, it is worth noting the intermediate level of digital competences reported by the teachers surveyed, with significant differences with respect to different key variables for their development. Therefore, we conclude by reflecting on the need to establish teacher training plans in this area.
THE INFLUENCE OF SOCIAL MEDIA ON CAMEROONIAN ENGLISH

Social media has emerged as a popular new technology that has spread all over the different sectors in Cameroon: education, business, government, print media, audiovisual media, etc. This new means of communication has eased the exchange of thoughts and ideas. The internet, as an aspect of new technologies, came along with a deviant form of writing peculiar to the younger generation in general and the Cameroonian youth in particular. This new system of communication gave rise to what is known as computer-mediated communication, which had become widely spread within the educational milieu in Cameroon. Social media has negatively influenced the use of Standard English in Cameroon, especially written texts, given that aspects of computer-mediated communication have rapidly replaced conversational writing forms. Such development in communication has affected language, which has gradually shifted from its original sound, sign, or symbol and complex sentence structures governed by grammatical rules to the language of text messaging with its simple syntax, incomplete sentence forms, informal structures, and modified ideograms known as emojis (happy faces, sad faces, excitement figures, blushing faces, etc.). This research aimed at analysing the writings of students at the University of Yaounde 1 to show the extent to which the use of social media has affected the written rendition of these students. To elucidate this, a study was done by analyzing their scripts and text messages. The communication accommodation theory was adopted to arrive at the expected results.
Investigation of Facilitating English Speaking of Elementary School Students in Authentic Contexts with UEnglish

Abstract Despite the importance of English in various aspects of life, students learning English as a foreign language often lack sufficient opportunities to practice speaking in their daily lives. To address these issues, this study proposes an innovative mobile app called Ubiquitous English (UEnglish) that facilitates English speaking learning in authentic contexts anytime and anywhere. Leveraging advanced technologies, UEnglish incorporates automatic speech recognition (ASR), cloud machine translation (MT), and text-to-speech (TTS) functionalities. The primary aim of this study is to assess the impact of UEnglish on students’ English speaking ability, specifically focusing on the quality and quantity of their spoken English when describing comic pictures and authentic pictures. To investigate the learning behaviors and the influence on English speaking learning achievement in this study, 56 fifth-grade students were employed, including 28 students in an experimental group (EG) using UEnglish and 28 students in a control group (CG) with a paper-based teaching method. The results indicated that, after the experiment, EG outperformed CG and EG had significant differences in the quality and quantity of English speaking in the context of describing comic pictures and authentic pictures. Students’ learning portfolios revealed that EG students not only could use more English words and more complex sentences to express their thoughts but also easily applied what they learned when encountering similar contexts. Our findings suggested that authentic contextual English learning with UEnglish was helpful for students to practice speaking English related to their surroundings.
Classifying seismograms using the FastMap algorithm and support-vector machines

Abstract Neural networks and related deep learning methods are currently at the leading edge of technologies used for classifying complex objects such as seismograms. However they generally demand large amounts of time and data for model training and their learned models can sometimes be difficult to interpret. FastMapSVM is an interpretable machine learning framework for classifying complex objects, combining the complementary strengths of FastMap with support vector machines (SVMs) and extending the applicability of SVMs to domains with complex objects. FastMap is an efficient linear-time algorithm that maps complex objects to points in a Euclidean space while preserving pairwise domain-specific distances between them. Here we invoke FastMapSVM as a lightweight alternative to neural networks for classifying seismograms. We demonstrate that FastMapSVM outperforms other state-of-the-art methods for classifying seismograms when train data or time is limited. We also show that FastMapSVM can provide an insightful visualization of seismogram clustering behaviour and thus earthquake classification boundaries. We expect FastMapSVM to be viable for classification tasks in many other real-world domains.
Single-Tiered Hybrid PoW Consensus Protocol to Encourage Decentralization in Bitcoin

We propose a single-tiered hybrid proof-of-work consensus protocol to encourage decentralization in bitcoin. Our new mechanism comprises coupled puzzles from which properties differ from each other; the one is the extant outsourceable bitcoin puzzle while the other is nonoutsourceable. Our new protocol enables miners to solve either puzzle as they want; therefore, blocks can be generated by either puzzle. Our hybrid consensus can be successfully implemented in bitcoin because it is backward-compatible with existing bitcoin mining equipment (more precisely, existing bitcoin mining ASICs).
A bio-inspired positional embedding network for transformer-based models

Owing to the progress of transformer-based networks, there have been significant improvements in the performance of vision models in recent years. However, there is further potential for improvement in positional embeddings that play a crucial role in distinguishing information across different positions. Based on the biological mechanisms of human visual pathways, we propose a positional embedding network that adaptively captures position information by modeling the dorsal pathway, which is responsible for spatial perception in human vision. Our proposed double-stream architecture leverages large zero-padding convolutions to learn local positional features and utilizes transformers to learn global features, effectively capturing the interaction between dorsal and ventral pathways. To evaluate the effectiveness of our method, we implemented experiments on various datasets, employing differentiated designs. Our statistical analysis demonstrates that the simple implementation significantly enhances image classification performance, and the observed trends demonstrate its biological plausibility.
Resource-aware video streaming (RAViS) framework for object detection system using deep learning algorithm

Video streams can come from various sources, such as surveillance cameras, live events, drones, and video-sharing platforms. Video stream mining is challenging due to the extensive resources needed to analyze and extract useful information from continuous video data streams. This situation could result in overwhelmed resources, which causes the system to stall. One of the ways to suffice the requirement is to provide larger resources, which leads to more costs. This research develops a data stream mining called the Resource-Aware Video Streaming (RAViS) framework to adapt to the limited resources (a Raspberry Pi) to run an object detection system using the YOLO algorithm. We validate the framework by capturing video streaming to simulate data streams. The video frames are processed using a deep-learning model to recognize the presence of a person(s) in a room. The RAViS framework adapts the object detection system to the availability of Raspberry Pi resources, such as CPU, RAM, and internal storage. The adaptation aims to increase the availability of resources to perform object detection of streamed video. The experimental results indicate that the RAViS framework can adapt the detection system to resource availability while maintaining accuracy. •A framework can ensure the availability of a computer with limited resources for running an object detection system using deep learning algorithms.•The framework constantly monitors the computer's memory, CPU, and storage, and provides feedback to the object detection system for adjusting its parameters to optimize resource utilization.•This approach enables the object detection system to operate continuously with the required resources, thus ensuring its accuracy and effectiveness.