Reviewerly/exHarmony huggingface dataset:
Children show adult-like memory patterns to scene images by the age of five

Which images are better remembered than others has been shown to be highly consistent across adults from different backgrounds, owing to an intrinsic memorability to images. While a number of studies have focused on image memorability among adults, no studies have examined how early children have gained sensitivity to these intrinsic features and determined at what point they start to show adult-like memory patterns to images. This study compared children’s memory of scene images to adults’ memory of the same images modeled by a deep neural network and tested behaviorally. We extracted data on children’s memory from Saragosa-Harris et al. (2021), where 137 children aged three to five years old first encoded animal-scene image pairs and were then cued with animals and asked to indicate the associated scenes from memory after a five-min, 24-h, or one-week delay. Adult memory was predicted by computing memorability scores of the same images using ResMem, a pre-trained deep neural network for predicting memorability (Needell & Bainbridge, 2021). Adults’ memory data was also acquired behaviorally on Amazon Mechanical Turk using a continuous recognition task. Results showed that the memory of scene images among five-year-old children showed significant correlations with the memory predictions by ResMem. ResMem scores were also most predictive of the memories of children across age groups after a one-week delay. These results suggest that adult-like patterns of scene image memories emerge in early childhood by the age of five. Further, these similarities to adults manifest most when memory is tested after a long, one-week delay.
Which salient features attract our gaze in fast vision of natural scenes?

The ability to process the information quickly and reliably in a visual scene is fundamental for survival. It is widely recognized that the visual system must operate an early and drastic input data reduction, to efficiently process relevant visual information within limited available resources. In past work, we used the principle of constrained maximum entropy to define a small number of local features that are optimal carriers of information. Experimentally, in fast vision conditions, we found that images where only optimal features are kept (sketches), and all the others are dropped, are still recognizable as the original versions. Here we explore whether these optimal local features still play an important role for gaze orientation and scene discrimination in more natural settings, where all the existing features are kept, but the overall information is reduced by showing only a fraction of the image. We briefly presented (25ms) a few small fragments, randomly extracted from natural images. Participants were invited to look at one fragment to be able to perform later a discrimination task (2IFC) between the full version of the image shown and a distractor. To reduce the contribution of global information on discrimination, in a second session, the contrast of the images could be randomly inverted. We found that eye movements head towards fragments containing more optimal features, especially in the session where image contrast could be inverted, and thus subjects had to presumably rely on local rather than global information. In this session, image discrimination performance was also significantly higher after saccades toward optimal features. Our results suggest that, in fast vision, optimal features, predicted by the constrained maximum-entropy model, are the preferential target of eye movements because of their saliency. These local features are also used for image discrimination when global information is not fully available.
Automated short answer grading with computer-assisted grading example acquisition based on active learning

The technology of automated short answer grading (ASAG) can efficiently process answers according to human-prepared grading examples. Computer-assisted acquisition of grading examples uses a computer algorithm to sample real student responses for potentially good examples. The process is critical for optimizing the grading accuracy of machine learning models given a budget of human effort and the appeal of ASAG to online learning providers. This paper presents a novel method called short answer grading with active learning (SAGAL) that features a unified formulation comprising the heuristics for identifying potentially optimal examples of representative answers, borderline answers, and anomalous answers. The method is based on active learning, which iteratively samples good examples and queries for annotation to increase the sampling accuracy. SAGAL has been evaluated with three different public datasets of distinctive characteristics. The results show that the resulting models generally outperform the baseline semi-supervised learning methods on the same number of grading examples.
Generalized Deep Latent Feature Learning

In this era of big data, people are surrounding serious problems of information overload [1–9]. It is important to develop an intelligent system to filter the desired information from big data. In many real big-data-related applications, such as recommender systems and social networks [10–12], the collected data are commonly high dimensional and incomplete (HDI) [13–16]. Hence, the crux of implementing such an intelligent system is to effectively and efficiently analyze the HDI data.
Crow algorithm based on variation and parameter recurrence

In order to improve the global search ability and easy to fall into local extrema, we propose a modified crow search algorithm (ISA) based on differential variation perturbation factor and dynamic recurrence rules of model parameters. Introduce the differential variation perturbation factor to accelerate the information exchange and improve the convergence rate, and then introduce the model parameter dynamic recurrence rule to improve the perception probability and flight length to balance the global search and local search. The experimental results of the ablation experiments on two engineering optimization design problems show that the optimization performance of the improved crow search algorithm improves significantly, which proves the effectiveness and correctness of the improved method.
Multi-Level Knowledge-Aware Contrastive Learning Network for Personalized Recipe Recommendation

Personalized recipe recommendation is attracting more and more attention, which can help people make choices from the exploding growth of online food information. Unlike other recommendation tasks, the target of recipe recommendation is a non-atomic item, so attribute information is especially important for the representation of recipes. However, traditional collaborative filtering or content-based recipe recommendation methods tend to focus more on user–recipe interaction information and ignore higher-order semantic and structural information. Recently, graph neural networks (GNNs)-based recommendation methods provided new ideas for recipe recommendation, but there was a problem of sparsity of supervised signals caused by the long-tailed distribution of heterogeneous graph entities. How to construct high-quality representations of users and recipes becomes a new challenge for personalized recipe recommendation. In this paper, we propose a new method, a multi-level knowledge-aware contrastive learning network (MKCLN) for personalized recipe recommendation. Compared with traditional comparative learning, we design a multi-level view to satisfy the requirement of fine-grained representation of users and recipes, and use multiple knowledge-aware aggregation methods for node fusion to finally make recommendations. Specifically, the local-level includes two views, interaction view and semantic view, which mine collaborative information and semantic information for high-quality representation of nodes. The global-level learns node embedding by capturing higher-order structural information and semantic information through a network structure view. Then, a kind of self-supervised cross-view contrastive learning is invoked to make the information of multiple views collaboratively supervise each other to learn fine-grained node embeddings. Finally, the recipes that satisfy personalized preferences are recommended to users by joint training and model prediction functions. In this study, we conduct experiments on two real recipe datasets, and the experimental results demonstrate the effectiveness and advancement of MKCLN.
Online Gain Tuning Using Neural Networks: A Comparative Study

This paper addresses the problem of adapting a control system to unseen conditions, specifically to the problem of trajectory tracking in off-road conditions. Three different approaches are considered and compared for this comparative study: The first approach is a classical reinforcement learning method to define the steering control of the system. The second strategy uses an end-to-end reinforcement learning method, allowing for the training of a policy for the steering of the robot. The third strategy uses a hybrid gain tuning method, allowing for the adaptation of the settling distance with respect to the robot’s capabilities according to the perception, in order to optimize the robot’s behavior with respect to an objective function. The three methods are described and compared to the results obtained using constant parameters in order to identify their respective strengths and weaknesses. They have been implemented and tested in real conditions on an off-road mobile robot with variable terrain and trajectories. The hybrid method allowing for an overall reduction of 53.2% when compared with a predictive control law. A thorough analysis of the methods are then performed, and further insights are obtained in the context of gain tuning for steering controllers in dynamic environments. The performance and transferability of these methods are demonstrated, as well as their robustness to changes in the terrain properties. As a result, tracking errors are reduced while preserving the stability and the explainability of the control architecture.
Faktor yang Mempengaruhi Minat Masuk Perguruan Tinggi bagi Siswa Sekolah Menegah Kejuruan

This study was carried out to determine how much interest and factors influence college entry for students in class XII of the SMK Electrical Power Installation Engineering Expertise Program at SMK Negeri 1 Tanjung Raya. This study employs descriptive analysis. The study's subjects were 44 students from class XII of the Electrical Power Installation Engineering Expertise Program at SMK Negeri 1 Tanjung Raya. A questionnaire was used to collect data. The validity test of the research instrument is carried out with construction validity, namely by testing the instrument items that have been compiled to experts called validators and using the factor analysis method. The Alpha formula is used to calculate the instrument reliability test based on internal consistency. The amount of interest is analyzed descriptively, and the factors that influence college interest are statistically tested using multiple regression in Excel. According to the study's findings, students in class XII SMK Electrical Power Installation Engineering Expertise Program at SMK Negeri 1 Tanjung Raya have a high interest in continuing their education, with an average percentage of 34%. These interests are influenced by percentage factors, namely the ideals factor (64%), the desire factor (57%), the motivation factor (37%), the family environment (52%), the family economic factor (32%), the relative factor (25%), the teacher factor (32%), the friend factor (36%), the school alumni factor (36%), and the school condition factor (39%).
Incorrect Computer Aided Detection (CAD) marks lead to early quitting: A potential mechanism for poor CAD performance in clinical practice

Prior work has shown that irrelevant distractors with an abrupt onset result in early quitting in a visual search task (Moher, 2020). Thus, while salient distractors often increase reaction time when a target is present, the same distractors elicit early quitting when no target is present. This may have important implications for applied tasks like medical screening, particularly in the context of CAD. In cancer screening, radiologists face an extremely difficult task where small differences could cost lives. There is a great deal of enthusiasm to help improve radiologist performance through AI-assisted cueing like CAD. Unfortunately, there are number of instances, such as breast cancer-screening, where CAD does not yield improved performance when deployed in the field. Prior work suggests that disappointing performance may be driven by attentional capture by CAD marks. The current examination sought to determine if the previously identified early quitting effect replicates in a situation more similar to CAD in screening radiology. Here, unlike Moher (2020), CAD marks sometimes highlighted targets (Ts embedded in 1/f noise) and appeared contemporaneously with the search array. Observers were divided into CAD and no CAD groups. We found that RTs greatly decreased (~500ms faster) on trials where there was an incorrect CAD mark on a distractor item (Ls). In addition, when a target was present and the CAD mark was on a distractor, accuracy was decreased relative to performance on the same targets in the No CAD condition. These results demonstrate that the previously identified phenomenon of early quitting in response to irrelevant distractor cues generalizes to a paradigm designed to emulate important aspects of breast cancer screening. More generally, these results suggest that early quitting may be an under-appreciated factor in understanding why CAD cues are less effective than expected in medical practice.
Performance Evaluation of Deep Learning Algorithm Using High-End Media Processing Board in Real-Time Environment

Image processing-based artificial intelligence algorithm is a critical task, and the implementation requires a careful examination for the selection of the algorithm and the processing unit. With the advancement of technology, researchers have developed many algorithms to achieve high accuracy at minimum processing requirements. On the other hand, cost-effective high-end graphical processing units (GPUs) are now available to handle complex processing tasks. However, the optimum configurations of the various deep learning algorithms implemented on GPUs are yet to be investigated. In this proposed work, we have tested a Convolution Neural Network (CNN) based on You Only Look Once (YOLO) variants on NVIDIA Jetson Xavier to identify compatibility between the GPU and the YOLO models. Furthermore, the performance of the YOLOv3, YOLOv3-tiny, YOLOv4, and YOLOv5s models is evaluated during the training using our PowerEdge Dell R740 Server. We have successfully demonstrated that YOLOV5s is a good benchmark for object detection, classification, and traffic congestion using the Jetson Xavier GPU board. The YOLOv5s achieved an average precision of 95.9% among all YOLO variants and the highest success rate achieved is 98.89.
PENGARUH KEKERASAN PADA CYBERBULLYING TERHADAP PERILAKU MASYARAKAT PADA MASA COVID-19

The purpose of this study was to determine the condition of cyberbullying that happened the city of Batam, especially during the ongoing Covid-19 pandemic. The types of research we use are qualitative and quantitative. This research was conducted with many respondents with a total of 250 respondents. The sampling technique used is the Cluster &amp; Stratified Random Sampling technique. The results showed that 30.8% often experienced or encountered cases of Cyberbullying and 45.2% had experienced or encountered cases of Cyberbullying. From the research, 62.8% experienced it in the school environment and 37.2% experienced it outside the school environment. This research can be a reference for readers in finding and determining the level of Cyberbullying cases in Batam city and how to handle them.
Direct and Indirect Relationships between Academic Self-efficacy, Teaching Presence, Behavior Regulation, and Perceived Achievement in a University Flipped Learning Environment

Objectives This study verified direct and indirect relationships among academic self-efficacy, teaching presence, behavior regulation, and perceived achievement in a university flipped learning enviornment in order to find how to design and operate a flipped learning with success. It adopted perceived achievement as a flipped learning outcome, and selected academic self-efficacy, teaching presence, and behavior regulation as predictors affecting perceived acheivement. Methods 91 students participating in a flipped learning class at a university in Gyeonggi-do completed surveys, and regression analysis was used to analyze direct and indirect effects between the variables. Mediating effects of teaching presence and behavior regulation were analyzed on the basis of Judd &amp; Kenny(1981)’s process analysis estimating mediation. Results The only direct relationship between teaching presence and perceived achievement was significant. The indirect relationship between academic self-efficacy, teaching presence, and perceived achievement was significant. The indirect relationship between academic self-efficacy, behavior regulation, and perceived achievement was not significant. Conclusions These findings imply that the instructor role to enhance learners’ a sense of duty, motivation, and learning engagement is very important in order to implement a effective flipped learning at a university.
A Study on the Teaching Method of Elementary Gukak Education Using Local Folk Songs: Focusing on the traditional children's song of Eumseong-gun &lt;kiwa palkki Song&gt;

Objectives The purpose of this study is to find an effective guidance plan for Korean traditional music education by utilizing the children's songs of Eumseong-gun among local folk songs in Chungbuk. Methods To this end, the theoretical background of the integrated subject-oriented curriculum and the music- oriented curriculum were theoretically examined, and the traditional nursery rhymes of Chungbuk were examined. Based on this, the traditional children's song “kiwa palkki Song” in Eumseong, Chungcheongbuk-do, was selected as a research song, and a total of 7 teaching and learning plans were integrated for 5th and 6th graders. Results It could be seen that it would be a guidance plan that meets the cultivation of creative and convergent talents aimed at the 2015 revised music curriculum, and the necessity of research on integrated education between subjects could be recognized. Conclusions Through this study, it is hoped that it will serve as an important foundation for learning our traditional music and understanding the essence, and furthermore, it was suggested that various follow-up studies on local folk songs in Chungbuk are continuously needed.
Research on the Application of Blockchain in the Quality Traceability of Power Resources and Materials

At present, power material companies have less quality information about key components of power materials.The quality information of these key components is stored in the centralized database of all parties in the power material supply chain.Any party in the supply chain can tamper with the quality information of a certain circulation link of key components by modifying the content of the database.Therefore, the centralized database recording method cannot guarantee the authenticity of relevant quality information of important components, and cannot trace the quality of key components.Blockchain technology has the characteristics of decentralization, data security, anonymity and data integrity, which can solve the above problems of supply chain quality traceability.The application of blockchain technology in the supply chain system increases the transparency of circulating information and ensures that the traceability information cannot be tampered with.Manufacturers, distributors and consumers join the blockchain network to jointly maintain the whole supply chain and ensure the transparency of data.The emergence of blockchain technology will greatly promote the development of power material supply chain.
Towards scalable scenarios human pose estimation via two-stage hierarchical network

Human pose estimation is a key step in understanding human behavior in images and videos. Bottom-up human pose estimation methods are difficult to predict the correct pose of a person in large scenes due to the challenge of scale variation. In this paper we propose a two-stage hierarchical network that first acquires images in large scenes, and sends tracking command signals to a two-degree-of-freedom shooting platform equipped with an image sensor to track a moving target based on a motion target detection frame, and locally constrains the captured image stream according to a top-down target detection algorithm to retain only the content related to the motion target in the image. The processed images are fed into the generalized human pose estimation model for pose detection. We deployed the algorithm on a two-degree-of-freedom filming platform equipped with camera equipment and deployed the experimental platform to sport scenes to conduct detection experiments on sport figures in running and ski jumping sport scenes, using the sport figure and its nearby area as the ROI region to generate pictures or videos with the skeleton pose of the sport target to guide the sport training of the target figure. This investigation can solve the challenge of scale variation to some extent in bottom-up multi-human pose estimation, especially for large scenes where the person key points can be located more accurately. The experiments show that this investigation can meet the practical use requirements of speed and accuracy of sport figure pose detection in large scenes of daily sports.
Deep learning-enabled single-shot fringe projection profilometry with composite coding strategy

In recent years, due to the rapid development of deep learning technology in computer vision, deep learning has gradually penetrated into fringe projection profilometry (FPP) to improve the efficiency of three-dimensional (3D) shape measurement and solve the problem of phase/or depth retrieval accuracy. In order to measure dynamic scenes or high-speed events, the single-shot fringe projection technique, due to its single-frame measurement property that can completely overcome the motion-induced errors of the object, becomes one of the optimal options. In this paper, we introduce a deep learning-enabled single-shot fringe projection profilometry with a composite coding strategy. By combining an FPP physical model-based network architecture with a large dataset, we demonstrate that models generated by training an improved deep convolutional neural network can directly perform high-precision phase retrieval on a single fringe image.
Commentary: Face-work in the digital ecology

This study examines polymorphism of second person singular forms of address (2PS) in the Spanish of Medellin, Colombia. Polymorphism of 2PS is the usage of more than one 2PS directed toward the same person during the same interaction. Speakers from Medellin (N = 72) carried out an oral discourse completion task that consisted of 24 situations. Results showed that polymorphism is common in Medellin Spanish as it occurred 22.7% of the time. It was also found that polymorphism is constrained by three variables: change in speech act, speaker sex, and interlocutor relationship. In particular, a change in speech act, male speakers, and intimate interlocutors favored polymorphism. In addition, characteristics of polymorphism are identified. Findings demonstrate that polymorphism is not random but rather systematic and purposeful—it allows speakers to express different speech acts and emotions via the shift in 2PS. Results are discussed in light of broader implications and the future of polymorphism in the Spanish of Medellin.