Reviewerly/exHarmony huggingface dataset:
Exploring the Use of Immersive Virtual Reality Games in a Formal School Environment

In recent years, immersive Virtual Reality (VR) has gained popularity among young users as a new technology for entertainment gaming. While VR remains majorly used for entertainment purposes, 3D desktop games are already used in schools. This study takes a closer look at the suitability for VR games to be used in a formal educational environment, and its potential to enrich existing game based learning approaches. Based on learning needs of in particular easily distracted and inattentive children, an immersive VR maths game was created and tested on 15 children aged 10–13. This study found VR to be suitable for usage in a school setting, as well as seeming to be an enjoyable addition to learning for this age group. Further research is needed to investigate its concrete potential.
Representation granularity enables time-efficient autonomous exploration in large, complex worlds

We propose a dual-resolution scheme to achieve time-efficient autonomous exploration with one or many robots. The scheme maintains a high-resolution local map of the robot's immediate vicinity and a low-resolution global map of the remaining areas of the environment. We believe that the strength of our approach lies in this low- and high-resolution representation of the environment: The high-resolution local map ensures that the robots observe the entire region in detail, and because the local map is bounded, so is the computation burden to process it. The low-resolution global map directs the robot to explore the broad space and only requires lightweight computation and low bandwidth to communicate among the robots. This paper shows the strength of this approach for both single-robot and multirobot exploration. For multirobot exploration, we also introduce a "pursuit" strategy for sharing information among robots with limited communication. This strategy directs the robots to opportunistically approach each other. We found that the scheme could produce exploration paths with a bounded difference in length compared with the theoretical shortest paths. Empirically, for single-robot exploration, our method produced 80% higher time efficiency with 50% lower computational runtimes than state-of-the-art methods in more than 300 simulation and real-world experiments. For multirobot exploration, our pursuit strategy demonstrated higher exploration time efficiency than conventional strategies in more than 3400 simulation runs with up to 20 robots. Last, we discuss how our method was deployed in the DARPA Subterranean Challenge and demonstrated the fastest and most complete exploration among all teams.
Completeness out of incompleteness: Inferences from regularities in imperfect information ensembles.

Handling imperfect information problems is fundamental to perception, learning, and decision-making. Ensemble perception may partially overcome imperfect information by providing global clues. However, if not all cluster elements are readily accessible, the observations required for computing statistics are incomplete. In this case, these elements' internal correlations (i.e., regularity) could serve as clues to elucidate the missing pieces. We thus investigated spatial regularity's role in ensemble perception under imperfect information situations created using partially occluded stimuli. In two experiments, we manipulated circle size (Experiment 1) and line orientation (Experiment 2) to linearly vary with its location; spatial regularity thus supplied clues for inferring information of the invisible parts. Participants estimated the mean of the targeted feature of the entire cluster, including visible and invisible parts. We observed robust biases toward the overall cluster in the estimations, implying the invisible parts were considered during ensemble perception. We proposed this effect could be understood as assessing evidence from visible parts to construct the missing parts. Experiment 3 employed a periodicity regularity to deter participants from using specific strategies, and consistent results were found. We then developed a generative model, the Regularity-Based Model, to simulate the inference process, which better captured the pattern of human outcomes than the comparative model. These findings indicate the visual system could use high-level structural information to infer scenes with incomplete information, thus producing more accurate ensemble representations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Syntactic-Informed Graph Networks for Sentence Matching

Matching two natural language sentences is a fundamental problem in both natural language processing and information retrieval. Preliminary studies have shown that the syntactic structures help improve the matching accuracy, and different syntactic structures in natural language are complementary to sentence semantic understanding. Ideally, a matching model would leverage all syntactic information. Existing models, however, are only able to combine limited (usually one) types of syntactic information due to the complex and heterogeneous nature of the syntactic information. To deal with the problem, we propose a novel matching model, which formulates sentence matching as a representation learning task on a syntactic-informed heterogeneous graph. The model, referred to as SIGN (Syntactic-Informed Graph Network), first constructs a heterogeneous matching graph based on the multiple syntactic structures of two input sentences. Then the graph attention network algorithm is applied to the matching graph to learn the high-level representations of the nodes. With the help of the graph learning framework, the multiple syntactic structures, as well as the word semantics, can be represented and interacted in the matching graph and therefore collectively enhance the matching accuracy. We conducted comprehensive experiments on three public datasets. The results demonstrate that SIGN outperforms the state of the art and also can discriminate the sentences in an interpretable way.
Sex and age differences in facial emotions expressions measured by artificial intelligence

Our aim was to test existing sex and age stereotypes related to emotional expressivity, gender and age.This was a complex analysis of facial expressions of all basic emotions (anger, disgust, fear, happiness, sadness, and surprise) to everyday life stimuli observing a large sample (2,969 unique participants creating 39,694 recordings) using an Emotion Artificial Intelligence.Our data partially support emotion-specific stereotype that women express more affiliate emotions and men express more dominant emotions except for sadness.There were found correlations of emotion expression with age, however intensity and frequency of emotion expression did not follow the same pattern.Not eliminating the differences between men and women in the baseline facial appearance resulted in men expressing dominant emotions (anger and disgust) more intensively, and women expressing more affiliative emotions (happiness, fear, and surprise).To sum up, facial appearance can be one of the origins of the existing gender stereotypic socialisation stereotype.
Performance Analysis of a Hybrid Security Algorithm for Secure Cloud Environment

Encryption algorithms are critical in ensuring the security of information in today's rapidly evolving internet and network applications. These algorithms are especially important in ensuring information security for internet-based dynamic applications and storage methods like cloud computing, which require high reliability, scalability, and autonomy to enable ubiquitous access. Given the prevalence of cloud computing in our everyday routines, data security has emerged as a major concern, particularly for non-technical users. To address this concern, the effectiveness of two widely used symmetric key encryption algorithms, AES and Blowfish, in terms of throughput, power consumption, and encryption speed, has been analyzed and compared. Based on the simulation results, it is clear that AES outperforms Blowfish when security flaws are put into consideration. It should be noted, however, that AES requires more processing power. As a result, a proposed efficient hybrid security algorithm seeks to combine the benefits of both approaches in an encryption system, thereby improving data security in a cloud environment while optimizing resource utilization. According to the performance analysis, the proposed hybrid approach is an excellent solution for encrypting and decrypting files with large block sizes and longer keys, as well as defending against Man in the Middle (MitM) attacks.
PEMBELAJARAN MENULIS PUISI MENGGUNAKAN METODE EXPERIENTIAL LEARNING BERBANTUAN MEDIA LAGU PADA PESERTA DIDIK KELAS X SMK PASUNDAN 1 BANDUNG

ABSTRACT&#x0D; This research is motivated by the low writing skills of students, which is caused by the lack of variation in teaching methods used by educators. The use of varied and creative teaching methods can make students more active and motivated during learning activities. One creative method that can be used is the experiential learning method. The objectives of this research are to test the author's ability to plan, implement, and evaluate poetry writing lessons using the experiential learning method for students of class X at Pasundan 1 Vocational High School in Bandung; to test the ability of students in class X at Pasundan 1 Vocational High School in writing poetry according to its structural elements; to determine the differences in poetry writing between the experimental class using the experiential learning method and the control class using the discussion method; and to test the effectiveness of the experiential learning method in poetry writing lessons for students of class X at Pasundan 1 Vocational High School in Bandung. In its implementation, the author used a quantitative method. The data obtained consisted of pretest and posttest scores of students from both the experimental and control classes. The findings of this research are as follows: 1) The author was able to plan, implement, and evaluate poetry writing lessons using the experiential learning method, with an average planning score of 3.7 and an average implementation score of 4, which falls into the category of very good. 2) The students' ability in writing poetry showed improvement. In the experimental class, the average pretest score of 57 increased to a posttest score of 77. In the control class, there was an improvement with an average pretest score of 36 increasing to a posttest score of 55. 3) There is a significant difference between the experimental class and the control class. 4) The experiential learning method is effective in teaching poetry writing. This is evidenced by the Mann-Whitney test results, which revealed a sig. value of 0.000 &lt; 0.05. Based on the above explanation, the experiential learning method can be considered as an effective alternative teaching method for writing poetry.&#x0D; Keywords: teaching method, experiential learning, writing, poetry.
Thangka Hyperspectral Image Super-Resolution Based on a Spatial–Spectral Integration Network

Thangka refers to a form of Tibetan Buddhist painting on a fabric, scroll, or Thangka, often depicting deities, scenes, or mandalas. Deep-learning-based super-resolution techniques have been applied to improve the spatial resolution of hyperspectral images (HSIs), especially for the preservation and analysis of Thangka cultural heritage. However, existing CNN-based methods encounter difficulties in effectively preserving spatial information, due to challenges such as registration errors and spectral variability. To overcome these limitations, we present a novel cross-sensor super-resolution (SR) framework that utilizes high-resolution RGBs (HR-RGBs) to enhance the spectral features in low-resolution hyperspectral images (LR-HSIs). Our approach utilizes spatial–spectral integration (SSI) blocks and spatial–spectral restoration (SSR) blocks to effectively integrate and reconstruct spatial and spectral features. Furthermore, we introduce a frequency multi-head self-attention (F-MSA) mechanism that treats high-, medium-, and low-frequency features as tokens, enabling self-attention computations across the frequency dimension. We evaluate our method on a custom dataset of ancient Thangka paintings and demonstrate its effectiveness in enhancing the spectral resolution in high-resolution hyperspectral images (HR-HSIs), while preserving the spatial characteristics of Thangka artwork with minimal information loss.
CHALLENGES FACED BY STUDENTS IN LEARNING SPEAKING SKILLS USING VIDEO CONFERENCING

Students are still using online resources in order to assist them with their studies during the post-pandemic period. In online language classes, online platforms are utilised as a tool for language learning, particularly in the development of speaking abilities. This study attempts to look into the difficulties encountered by a group of bachelor degree students when learning speaking skills in the English language through the usage of video conferencing. The information was gathered by utilising a set of questionnaires distributed among 102 bachelor degree students at Universiti Teknologi MARA, Perlis Branch, Malaysia, towards the end of the semester after they had completed the speaking exercises that had been assigned to them. The use of video conferencing in teaching speaking skills to the students was found to pose a number of difficulties, including unfavourable environment, lack of ideas and vocabulary, lack of confidence, fear of making mistakes, and technological difficulties. The findings of this study may assist language instructors to choose the best online tool for speaking lessons.
A Systematic Study of Intercultural Listening Strategies in Higher Education Institutions in the Context of Big Data

Abstract As society keeps moving forward, intercultural listening strategy is the way to promote international communication skills, and big data technology is needed to provide technical support for intercultural listening strategy in higher education institutions. In this paper, based on the logistic regression algorithm in the context of big data, an intercultural listening strategy model for higher education institutions is established, and the performance of the model is tested in a ten-fold cross-validation experiment, and four indicators are proposed in the intercultural listening strategy model to verify the effectiveness of the model. From the data, the average precision, average recall, average -value, and average -value of the ten sets of test data of the logistic regression model based on big data technology was 77.26%, 77.81%, 77.93%, and 77.56%, respectively, which indicated that all the indicators of the model met the expected performance requirements. From the four example indicators, the mean values of intercultural cognition, intercultural attitude, intercultural behavioral orientation, and listening ability were 58.2%, 60%, 60.61%, and 61.49%, respectively, and overall the higher education institutions had a clear understanding of intercultural listening strategies. The logistic regression model based on big data technology can well analyze the good and bad of current intercultural listening strategies in higher education institutions, and based on the analysis results, we can better improve the deficiencies and then promote the update of intercultural listening strategies in higher education institutions so that students in higher education institutions can show their confidence in the process of international communication and improve their intercultural communication skills.
Innovative research on the construction of experimental teaching platform of economics and management in colleges and universities based on the background of big data

Abstract Under the wave of big data technology development, the traditional experimental teaching methods in colleges and universities are difficult to meet the requirements of the educational objectives of the times, and teaching innovation is the focus of teaching research in many colleges and universities, which has attracted the attention of the majority of educational researchers. This paper focuses on the innovation of the construction of experimental teaching platforms of economic management in colleges and universities based on the background of big data. This paper explains the necessity of innovation of economic management experimental teaching platform, proposes the basic framework of innovation of economic management experimental teaching platform (Kube storage cloud data, front-end design, collaborative filtering algorithm), and researches and discusses the innovation mode and operation mechanism of economic management experimental teaching platform based on experimental teaching construction, based on the similarity between the specified time point and other time points The impact of the time factor on the recommendation results of experimental resources is analyzed. The results of experimental teaching resources recommendation reflect the resource recommendation ability of this platform and compare with H5 technology experimental teaching platform and content analysis method experimental platform to analyze the changing trend of MAE of teaching resources recommendation; analyze the number of concurrent connections and clicks of this platform to verify the performance of this platform. The research results show that this paper platform’s experimental teaching resources recommendation effect with MAE value of 0.51 is better than other platforms. The reason is that this paper platform’s experimental teaching resources recommendation fully takes into account the time factor and co-rated experimental teaching resources, so the accuracy of experimental teaching resources recommendation is significantly improved. This study can improve the quality and teaching efficiency of universities and train more excellent economic management talents for the country, which is of great historical significance to the development of China’s economy and education.
Research on the design and application of intelligent teaching management system of intelligent training room in the background of internet

Abstract Higher vocational colleges and universities have recently been booming from number to scale. While the increase in enrollment scale and equipment value has brought great benefits to schools and greatly improved their teaching capacity, it has also brought problems to equipment management. In traditional practical training room management, problems such as low equipment utilization, work efficiency, and teaching efficiency are encountered. Along with the development of Internet technology, real-time monitoring and intelligent management of practical training rooms are possible. This paper firstly thus proposes the logical level, management dimensions, and information interaction contents of the intelligent, practical training management system to establish the architecture of the intelligent, practical training management system, and thus leads to the key technologies and methods for the design and establishment of the intelligent, practical training management system. Secondly, starting from the analysis of the training process, the bottom physical environment is sorted out in detail. The four dimensions of equipment status, consumables status, energy consumption status and teaching status of the smart training management system are collected in the information. The smart training management system model is established to analyze the data in a single dimension and derive effective information to help managers manage collaboratively. Finally, the model’s effectiveness is verified by applying the smart training room management system in Z higher education institutions in Xiamen. The study of Xiamen Z higher vocational institution in this paper shows that the use of an intelligent teaching management system effectively improves performance by 34.12%, the affiliation and utility rate of an Bar and coffee training room is better than the traditional training platform, and the influence parameter on the learning process of Xiamen Z higher vocational institution is found to be 79.62 through classification, which provides effective decision for teachers to implement differentiated teaching. The intelligent teaching management system proposed in this paper realizes data visualization of the training room equipment, provides data support for teachers to set differentiated teaching programs, improves teaching efficiency, improves students’ practical training skills through data-based analysis of problems in students’ practical training, and provides motivation for talent training in universities.
Thinking about the strategy and practice path of modern agricultural industry development in the context of big data

Abstract China is in the critical period of “four synchronous development” of industrialization, informatization, urbanization, and agricultural modernization, so it is urgent to find a correct, scientific, and reasonable development strategy for modern agricultural products and promote the development of big data agriculture. In this paper, we use big data technology to determine the algorithm model of agricultural big data technology and the application system of the agricultural Internet of Things and argue for big data for agricultural planting technology, agricultural economic management, and agricultural industry upgrading in order to find the optimal strategy for the development of modern agriculture in China. As of the statistics at the end of 2019, China’s arable land transfer area has reached 440 million mu, accounting for 30.8% of the total contracted arable land area. With the subsequent land transfer entering a standardized and normalized stage, the scale operation of agricultural production is bound to speed up. In recent years, due to the application of big data technology in agricultural production, China’s modern agricultural industry has developed rapidly, with the mechanization rate of farming at around 86% and the contribution rate of science and technology at over 67%, and agricultural production has gained breakthroughs nationwide. Thus, it can be seen that the modern agricultural industry in the context of big data will usher in new development opportunities.