Reviewerly/exHarmony huggingface dataset:
The Students’ Perception toward Learning Platform of Quizizz in Learning Vocabularies at MAN 1 Malang

The education system in Indonesia has been impacted by the spread of Covid-19. Learning occurs not only in real objects but also in cyberspace, such as through internet intermediaries. This requires the adaptation and sustainability of the teaching-learning process at all levels of guidance. Online learning has been designated as a distance learning management system by the Indonesian government. The sample of this research was 89 sample of the eleventh-grade students of Madrasah Aliyah Negeri I of Kabupaten Malang while the samples is taken from three different classes, those are XI IPA, XI IPS and XI Agama. The quantitative method was used in this study. To collect the data, questionnaires was used as the primary tool. The questionnaires is analyzed and see how students perceive toward learning platform of quizizz in learning vocabularies at MAN 1 Kabupaten Malang. Closed-ended data analysis was utilized to determine the outcome of the students’ perspective and to verify the established data, and the last steps taking conclusion. The results showed statement No. 5 achieved the highest mean rank of 77,8 by getting the average value at which most participants agree that Doing test in Quizizz is fun and the instruction easy to understand. Statement No. 11, which indicates that most participants feel that taking an exam on Quizziz saves greater time than taking one on paper and pencil, had the second highest mean. With an average of 66,85, question number seven had the least mean value of all the questionnaire statements. There were several responses to this question, with the highest percentages being 56.2% agreeing, 5.6% strongly agreeing, and 38.2% disagreeing. It seems students can help to concentrate in class when using Quizizz but some might feel the opposite Future studies are expected to improve the suggestions that are expected to be better in this suggestion. Other applications, such as Kahoot, Zoom, Moodle, YouTube, and others, can also be used in online classes as media to facilitate the learning process and student discussions with teachers.
Convoluted Neighborhood-Based Ordered-Dither Block Truncation Coding for Ear Image Retrieval

Image retrieval is a significant and hot research topic among researchers that drives the focus of researchers from keyword toward semantic-based image reconstruction. Nevertheless, existing image retrieval investigations still have a shortage of significant semantic image definition and user behavior consideration. Hence, there is a necessity to offer a high level of assistance towards regulating the semantic gap between low-level visual patterns and high-level ideas for a better understanding between humans and machines. Hence, this research devises an effective medical image retrieval strategy using convoluted neighborhood-based Ordered-dither block truncation coding (ODBTC). The developed approach is devised by modifying the ODBTC concept using a convoluted neighborhood mechanism. Here, the convoluted neighborhood-based color co-occurrence feature (CCF) and convoluted neighborhood-based bit pattern feature (BBF) are extracted. Finally, cross-indexing is performed to convert the feature points into binary codes for effective image retrieval. Meanwhile, the proposed convoluted neighborhood-based ODBTC has achieved maximum precision, recall, and f-measure with values of 0.740, 0.680, and 0.709.
Create a dynamic teaching atmosphere for distance learning with secure virtual classroom

As the new crown epidemic has gradually become a normalized disease similar to the flu, people are paying more and more attention to remote work and learning. Companies such as meta have spotted the development of the remote social track, and positioned the overall development direction of the company as the research and development of the metaverse virtual space. During the epidemic, the school’s population base was large, and the students’ demand for distance learning was also large. In the current distance teaching, students’ sense of participation, concentration, and learning effect were poor. With meta company’s all-round research on virtual technology, VR and other technologies are rapidly being applied in various industries. Among them, the application of VR technology to the creation of virtual classrooms has become a research hotspot in academia, but the probability of exposure of students’ personal information to virtual classrooms how to ensure the security of students’ information while carrying out efficient teaching in virtual classrooms is the focus of this paper. This paper first introduces VR and blockchain technology and their corresponding applications, and then uses a questionnaire to obtain the current information security issues and teaching atmosphere of distance learning, and summarizes the current distance learning for students and teachers according to the questionnaire. needs and actual problems. Finally, combined with the characteristics of VR virtual technology, a virtual classroom is designed to solve the current problems of distance learning. After a simulation test, it was found that the distance teaching classroom using virtual reality technology can achieve the same or even better teaching effect than the traditional offline classroom.
Implementation of a Hardware-Assisted Bluetooth-Based COVID-19 Tracking Device in a High School: Mixed Methods Study

Background Contact tracing is a vital public health tool used to prevent the spread of infectious diseases. However, traditional interview-format contact tracing (TCT) is labor-intensive and time-consuming and may be unsustainable for large-scale pandemics such as COVID-19. Objective In this study, we aimed to address the limitations of TCT. The Yale School of Engineering developed a Hardware-Assisted Bluetooth-based Infection Tracking (HABIT) device. Following the successful implementation of HABIT in a university setting, this study sought to evaluate the performance and implementation of HABIT in a high school setting using an embedded mixed methods design. Methods In this pilot implementation study, we first assessed the performance of HABIT using mock case simulations in which we compared contact tracing data collected from mock case interviews (TCT) versus Bluetooth devices (HABIT). For each method, we compared the number of close contacts identified and identification of unique contacts. We then conducted an embedded mixed methods evaluation of the implementation outcomes of HABIT devices using pre- and postimplementation quantitative surveys and qualitative focus group discussions with users and implementers according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance framework. Results In total, 17 students and staff completed mock case simulations in which 161 close contact interactions were detected by interview or Bluetooth devices. We detected significant differences in the number of close contacts detected by interview versus Bluetooth devices (P&lt;.001), with most (127/161, 78.9%) contacts being reported by interview only. However, a significant number (26/161, 16.1%; P&lt;.001) of contacts were uniquely identified by Bluetooth devices. The interface, ease of use, coherence, and appropriateness were highly rated by both faculty and students. HABIT provided emotional security to users. However, the prototype design and technical difficulties presented barriers to the uptake and sustained use of HABIT. Conclusions Implementation of HABIT in a high school was impeded by technical difficulties leading to decreased engagement and adherence. Nonetheless, HABIT identified a significant number of unique contacts not reported by interview, indicating that electronic technologies may augment traditional contact tracing once user preferences are accommodated and technical glitches are overcome. Participants indicated a high degree of acceptance, citing emotional reassurance and a sense of security with the device.
Deep Weighted Guided Upsampling Network for Depth of Field Image Upsampling

Depth-of-field (DoF) rendering is an important technique in computational photography that simulates the human visual attention system. Existing DoF rendering methods usually suffer from a high computational cost. The task of DoF rendering can be accelerated by guided upsampling methods. However, the state-of-the-art guided upsampling methods fail to distinguish the focus and defocus areas, resulting in unsatisfying DoF effects. In this paper, we propose a novel deep weighted guided upsampling network (DWGUN) based on a encoder and decoder framework to jointly upsample the low-resolution DoF image under the guidance of the corresponding high-resolution all-in-focus image. Due to the intuitive weight design, the traditional weighted image upsampling is not tailored to DoF image upsampling. We propose a deep refocus-defocus edge-aware module (DREAM) to learn the spatially-varying weights and embed them in the deep weighted guided upsampling block (DWGUB). We have conducted comprehensive experiments to evaluate the proposed method. Rigorous ablation studies are also conducted to validate the rationality of the proposed components.
Asymmetric Label Propagation for Video Object Segmentation

Semi-supervised video object segmentation aims to segment foreground objects across a video sequence based on their masks given at the first frame. The motion in adjacent frames tends to be smooth, yet object appearances could change substantially in subsequent frames due to clutters or occlusions. Most existing works segment a video frame by equally referring to segmentation masks of its previous frame and the first frame, and are prone to unreliable matching and accumulated segmentation errors. In order to alleviate this issue, this paper proposes to treat the first and previous frames differently to leverage the motion and appearance clues reliably, and presents an Asymmetric Label Propagation (ALP) method. ALP consists of a Confidence-guided Local Propagation (CLP) module and a Global Label Matching (GLM) module, respectively. CLP propagates labels from the previous frame to the current frame based on local affinity and appearance matching uncertainty. To further recover potential missing objects and alleviate error accumulation, GLM matches the current frame to both the foreground and background of the first frame, and adaptively fuses their matching results. The CLP and GLM outputs are fused to generate object-specific feature maps to perform multi-object segmentation. Extensive experiments on DAVIS and Youtube-VOS datasets demonstrate the effectiveness of the proposed method.
Development and use of modern mobile applications for training

Abstract The article describes the development and introduction of a mobile application based on Android using Java programming language the educational process of students. An overview of applications for teaching the subject “Probability theory, mathematical statistics and random processes” and for teaching solving problems with parameters is given. The main stages of working on a mobile app are described in details, the advantages and disadvantages of the research are indicated.
The minor poet: a case of John Wieners

There are many kinds of minor poets, but one subcategory can be defined by a particular predicament: being at risk of both extinction and success. While the threat of obscurity is intuitive, this essay shows how the latter is equally salient, as exemplified in the case of John Wieners. Rather than asking whether minor poets can become ‘spoiled’ when they achieve popularity, this article argues that we should ask what in their work produces a certain anxiety over spoilage in the first place. In the case of Wieners, the poet's minor status rises to the surface precisely at the moment when he emerges as potentially over-exposed, which always already seems to be the case, because that risk is built into his very poetics of need and dependency. Wieners generates a relationship to the reader that asks to be seen and covered up at the same time.
ResViT

Deepfake makes it quite easy to synthesize videos or images using deep learning techniques, which leads to substantial danger and worry for most of the world's renowned people. Spreading false news or synthesizing one's video or image can harm people and their lack of trust on social and electronic media. To efficiently identify deepfake images, we propose ResViT, which uses the ResNet model for feature extraction, while the vision transformer is used for classification. The ResViT architecture uses the feature extractor to extract features from the images of the videos, which are used to classify the input as fake or real. Moreover, the ResViT architectures focus equally on data pre-processing, as it improves performance. We conducted extensive experiments on the five mostly used datasets our results with the baseline model on the following datasets of Celeb-DF, Celeb-DFv2, FaceForensics++, FF-Deepfake Detection, and DFDC2. Our analysis revealed that ResViT performed better than the baseline and achieved the prediction accuracy of 80.48%, 87.23%, 75.62%, 78.45%, and 84.55% on Celeb-DF, Celeb-DFv2, FaceForensics++, FF-Deepfake Detection, and DFDC2 datasets, respectively.
Robust programmable PIC platform based on topological photonic insulator lattice

We propose a robust programmable photonic integrated circuit platform based on a 2D Floquet topological microring lattice. The topological optical FPGA can realize general photonic circuits with fewer control elements than conventional waveguide networks while providing the advantage of robustness to random device variations.
3D memory priors reflect efficient compression in view naming

Serial reproduction reveals that human memory for the 3D views of everyday objects tends to be biased towards canonical views, such as the top, bottom, front, back and side views of the object. We propose a theory of human memory for 3D views based on Shannon’s classical communication model that explains these biases in terms of the efficient compression of visual percepts into discrete categories that are transmitted by a speaker to a listener. We show that incremental memory biases revealed by serial reproduction are predicted by a model that efficiently compresses perspective views into view categories by optimizing the information bottleneck (IB) trade-off between the complexity and accuracy of a lexicon of possible view categories. Our results challenge the frequency hypothesis, which argues that canonical views for an object correspond to the views that are the most commonly encountered for that object. Instead, our proposal suggests that canonical views correspond to the optimal compression of all possible views into a discrete set of views that reflects the optimal trade-off between the complexity and accuracy of the representation.
Role of low level and high level factors during task-based scene viewing

Eye movements are guided by both low level (image salience) and high level (semantic salience) factors. To investigate the contribution of low and high level factors in task-based natural scene viewing, we tracked gaze while subjects performed imaginary tasks and as they searched for target objects. Image salience (GBVS) was used to estimate low level salience of each pixel, semantic salience was estimated from the linguistic similarity (LSA) between labeled objects and the search term. In the imaginary task, participants (N=40) observed 10 scenes as if they were performing one of two tasks assigned to each scene. In the search task, participants (N=40) searched 100 scenes for a target object, clicked on it if they located it, or clicked “no object” if the target was absent. We hypothesized that the influence of low and high-level determinants of gaze selection would vary within each trial as subjects grasped the context of the scene. In the imaginary task, we compared ROC values for gaze overlapped with the matching LSA heatmap of the presented task (matched), against ROC values for the same gaze but the alternate task (opposite). Semantic salience at fixated locations was significantly higher for matched gaze and task, compared to opposite gaze and task (t(39) =10.861; p<.001). In the search task, linear mixed model analysis showed a significant interaction between salience type and fixation number (X²(1, N=40) = 47.501, p <.001), where image salience decreased with increasing fixation number. Semantic salience remained constant throughout trials. This suggests that fixations are decreasingly guided by image salience over time, while fixations are steadily guided by semantic salience. Together, these results suggest that semantic salience is a useful predictor of gaze during task related scene viewing.
Can thermal fingerprints of Mediterranean terrestrial orchid seeds be used to optimize ex situ conservation?

This paper is concerned with the parameter estimation of a class of time-delay systems in the state space form. By using the hierarchical identification principle, a gradient based and a least squares based iterative identification algorithms are developed to compute these parameter estimates. The basic idea is to decompose a state space system into two subsystems, one containing a parameter vector and the other containing a parameter matrix. The simulation example demonstrates the efficacy of the proposed theory.
Kinematic modes of vision-based heterogeneous UAV-AGV system

UAV-AGV (Unmanned Aerial Vehicle-Autonomous Ground Vehicle) heterogeneous multi-agent system is gaining attention of researchers and became the topic of interest. UAV-AGV system is more advantageous than individual robots as both mobile platforms benefit from independent capabilities. Vision-based technique is the most researched method to build collaborative connection between both agents as it can function in GPS inaccessible areas. Take-off, tracking, and landing are basic tasks of the system. Vision-based solutions for these tasks are given most attention by various researchers so far. However, complex collaborative kinematics between agents are necessary to accomplish the complicated tasks which requires further investigation of the system. Moreover, a detailed kinematic model is necessary to better describe a robotic system which still needs to be developed to the vision-based UAV-AGV system. Kinematic analysis of the system is performed in the present work to address these issues. A differential form of mathematical representation is obtained for the vision-based collaboration between UAV and AGV. Forward and inverse kinematic notions are introduced for the system. Three kinematic modes of the system are identified and explained based on the kinematic model. Third mode reveals an interesting relative motion and novel behaviour of the system. Further step is taken to implement sliding mode-based control theory to develop novel kinematic controllers for the motion of UAV using each kinematic mode. Kinematic controllers are validated with thorough simulations. The performance of the controllers is acceptable and may be applied to activate the different modes of the system depending on the mission requirement.
Digital Extensive Reading in Indonesia: A Critical Review

&lt;em&gt;Several previous studies have revealed that extensive reading is essential for supporting the success of EFL students’ language learning. Due to the advancement of technology that has entered the language classroom, the implementation of extensive reading programs in Indonesia has shifted to digital extensive reading, where both EFL students and teachers utilize digital materials or tools through various applications or online platforms. Hence, this paper aims to critically review digital extensive reading in Indonesia to provide an overview or information regarding the implementation of digital extensive reading in Indonesia.&lt;/em&gt; &lt;em&gt;Following George’s (2008) Literature Reviews model, twelve articles related to digital extensive reading in Indonesia that were published from 2019 to 2022 in reputable national journals were critically reviewed. It was revealed that the digital extensive reading in Indonesia was implemented variously through several digital or online platforms for extensive reading, such as Let’s Read! application, Blogging, Mobile-Assisted, Xreading, Wattpad, Webtoon, and other various online platforms, such as WhatsApp, Zoom, and Google Classroom.&lt;/em&gt; &lt;em&gt;Last but not least, it can be concluded that the implementation of digital extensive reading through those various digital or online platforms has been proven to be useful in supporting the success of EFL students’ digital extensive reading activities as well as the success of EFL students’ language learning.&lt;/em&gt;
Vision measuring technology for the position degree of a hole group

The hole is one of the most important geometric elements in mechanical parts. The center distance of a hole group measurement method based on machine vision is proposed for solving the influence of perspective distortion and improving the applicability of vision systems. In the method, the plane equation of the measured plane is obtained by the line structured light vision technology, and the process is free from the constraints of the calibration plate. In order to eliminate the effect of projection distortion on the measurement accuracy, a local coordinate system is established on the plane of the measured hole group, the hole diameter, and the center distance of the hole group, which could be calculated by the local coordinates of the hole edge points. In the experiment, the flange is taken as the measured object, the distances between the holes on the flange are obtained by the method proposed in this paper, and the measurement results compared with the data are obtained by a coordinate measuring machine (CMM). The experimental results show that the average measurement error of center distance is 0.0739 mm, and the standard deviation is 0.0489 mm.
Does using a diversity of graph types help your audience remember your data?

Visual memory is typically better when the memorized objects are different in their categories and features, presumably because their representations have less overlap and interference (Konkle et al., 2010). If true, this effect might have implications for improving memory in real world situations, such as trying to remember multiple patterns of data shown across the sequence of a research presentation. We tested whether participant memory was better for data contained across three sets of data visualizations of different types (a bar graph, pie graph, and a bubble graph), as opposed to when they were of the same type (three bar graphs, three pie graphs, or three bubble graphs). Participants (N=48) saw sets of three graphs of different types, and three of the same type. After each triplet they reproduced the four plotted values of each of the three graphs by drawing on a blank 'template' containing each graph's title, axes, and data labels. The reproduction error showed a non-significant trend toward a small memory advantage (10% error became 8% error) for the different set graphs, suggesting that, in this initial study, showing data across different visualization formats results in a tiny memory advantage at best.