Reviewerly/exHarmony huggingface dataset:
Handling Non-Visible Referents in Situated Visualizations

Situated visualizations are a type of visualization where data is presented next to its physical referent (i.e., the physical object, space, or person it refers to), often using augmented-reality displays. While situated visualizations can be benefcial in various contexts and have received research attention, they are typically designed with the assumption that the physical referent is visible. However, in practice, a physical referent may be obscured by another object, such as a wall, or may be outside the user's visual feld. In this paper, we propose a conceptual framework and a design space to help researchers and user interface designers handle non-visible referents in situated visualizations. We frst provide an overview of techniques proposed in the past for dealing with non-visible objects in the areas of 3D user interfaces, 3D visualization, and mixed reality. From this overview, we derive a design space that applies to situated visualizations and employ it to examine various trade-offs, challenges, and opportunities for future research in this area.
FSLens: A Visual Analytics Approach to Evaluating and Optimizing the Spatial Layout of Fire Stations

The provision of fire services plays a vital role in ensuring the safety of residents' lives and property. The spatial layout of fire stations is closely linked to the efficiency of fire rescue operations. Traditional approaches have primarily relied on mathematical planning models to generate appropriate layouts by summarizing relevant evaluation criteria. However, this optimization process presents significant challenges due to the extensive decision space, inherent conflicts among criteria, and decision-makers' preferences. To address these challenges, we propose FSLens, an interactive visual analytics system that enables in-depth evaluation and rational optimization of fire station layout. Our approach integrates fire records and correlation features to reveal fire occurrence patterns and influencing factors using spatiotemporal sequence forecasting. We design an interactive visualization method to explore areas within the city that are potentially under-resourced for fire service based on the fire distribution and existing fire station layout. Moreover, we develop a collaborative human-computer multi-criteria decision model that generates multiple candidate solutions for optimizing firefighting resources within these areas. We simulate and compare the impact of different solutions on the original layout through well-designed visualizations, providing decision-makers with the most satisfactory solution. We demonstrate the effectiveness of our approach through one case study with real-world datasets. The feedback from domain experts indicates that our system helps them to better identify and improve potential gaps in the current fire station layout.
Malevolent Creativity? The Science of Evil Innovations: Interview With Gayle Dow, PhD

You might think of creativity as inherently good. But guess again! This Halloween season, guest speaker Dr. Gayle Dow unleashes the science of malevolent creativity. Treat yourself to a new understanding of the characteristics and behaviors of internet scammers, conniving workplace employees, terrorists, serial killers, and more.
Weakly Nonlinear Time Invariant Systems

Abstract As outlined in Chap. 1 , the behavior of nonlinear systems is substantially richer than the one of linear systems. To deal with them there is a set of techniques, each one best suited to analyse particular aspects or particular classes of nonlinear systems. We target systems that are stable about an equilibrium point and that depend continuously on the input signal.
Content-aware Fine-grained Sparse Transformer for Single Image Super-resolution

Single image super-resolution (SR) is a classical low-level vision task, which has been investigated for many years. With the development of deep learning (DL), single image SR has achieved the promising performance in the past decade. Nowadays, DL-based single image SR methods roughly can be divided into CNN-based SR and Transformer-based SR. Current Transformer-based single image SR methods can model the long-range dependencies and capture the no-local self-similarity inside image over the CNN-based ones. Therefore, Transformer-based single image SR methods achieve the state-of-the-art performance. However, Transformer-based SR methods generally sample image tokens densely and calculate the multi-head self-attention (MSA) among all the tokens, which has the large computation costs. In this paper, we combine the sparsity nature of SR task and the powerful long-range modeling capability of Transformer to improve the efficiency and scalability of the Transformer-based SR methods. Specifically, we propose a content-aware fine-grained sparse Transformer (CFST) network for single image SR. Our CFST can efficiently calculate the MSA by clustering the related tokens in content and reduce the unnecessary computation between irrelated tokens. Extensive experiments show that our CFST can achieve the superior SR performance with less computational costs on SR benchmark datasets.
Designing for Children's Social Play in Responsive Multi-sensory Environments

A responsive multi-sensory environment is designed to create an engaging experience by integrating various sensory stimuli such as sight, sound, and touch. Most prior research in the Human-computer Interaction (HCI) field has investigated its use for children with disabilities (e.g., autism) or for personal play or dyadic activities. Our work focuses on the use of responsive media for children without disabilities in primary education contexts and within a group of children. Our goal is to evaluate the capabilities of responsive multi-sensory environments, integrating wearable garments to foster children's social interaction behavior and collaboration. The enhanced garments we are developing will feature multi-channel haptic actuators, allowing players to feel tactile sensations corresponding to the proximity and activity of other peers. Additionally, virtual creatures will be projected visually an acoustic floor, which players will be able to interact with. Our design involves a series of aquatic creatures that display different "greeting" behaviors as participants stand in different proximity zones to each other.
Readiness of Teachers of Educational Organizations to Popularize, Disseminate and Implement Psychological and Pedagogical Knowledge in Educational Practice

The article is intended to raise the issue of application of scientific psychological and pedagogical knowledge by teachers of educational organizations to enrich modern pedagogical practice. Such criteria of teachers’ readiness for popularization, dissemination and introduction of psychological and pedagogical knowledge (achievements and developments of psychological and pedagogical science) into educational practice as affil-iative, activity and amplificative are analyzed. The aim of the study is to estimate the efficiency of existing mechanisms and channels of popularization, dissemination and implementation of achievements and devel-opments of psychological and pedagogical science in pedagogical activities. The authors organized and con-ducted a study of teachers’ readiness to generalize their pedagogical experience and pass it on to their col-leagues, to disseminate scientific psychological and pedagogical knowledge among students and their par-ents. For this purpose the method of survey in a written form – questionnaire was used. In accordance with the selected criteria, the analysis of the results of the conducted research of teachers’ readiness to popularize, dis-seminate and implement psychological and pedagogical knowledge in educational practice is presented.
The Effectiveness of Teaching Reading Skills by Using Cartoon Clips/ Comic Strips in Saudi Context

This paper conducts a study to assess the effectiveness of reading strategies such as using visual aids like comic strips at the secondary school level, KSA. The objective of this research is to highlight the need to use visual aids such as cartoon clips and comic strips, which makes reading comprehension a creative process. This research will be beneficial for EFL reading skill students by better understanding, word processing, and forming relations with the text. The purpose of this research is to show better and more effective techniques implacable in a Saudi Context. In this research, the researcher will use the survey method and a questionnaire as a tool to collect data from a random sample of 50 students. This paper will make suggestions and recommendations for future study and will teach new pedagogical methods through graphic display.
Analysis of Employee Performance and factor Influencing It Using the C4.5 Algorithm Method

In an organization, individual performance greatly affects the performance of the organization, both government and private organizations in achieving predetermined goals. Good employee performance will have an impact on overall organizational performance, which in turn can be seen from organizational achievements. Some important factors that are taken into consideration by an employee performing well, among others; Employee salary, incentives/bonuses, work motivation, workload/quantity of work, reliability, and employability. This study uses a data mining classification approach using the c4.5 method in making a decision tree. The results of a trial of 25 datasets using the decision tree method were able to provide a conclusion that employees will be able to improve their performance if the organization provides a policy in the form of providing monthly incentives to employees as a bonus reward to those who are diligent in carrying out their work, such as having activeness and being able to work together.
View these Featured Solutions

Inside Precision MedicineVol. 10, No. 5 MarketplaceView these Featured SolutionsPublished Online:26 Oct 2023https://doi.org/10.1089/ipm.10.05.12AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View article"View these Featured Solutions." Inside Precision Medicine, 10(5), p. 55FiguresReferencesRelatedDetails Volume 10Issue 5Oct 2023 InformationCopyright © GEN PublishingTo cite this article:View these Featured Solutions.Inside Precision Medicine.Oct 2023.55-55.http://doi.org/10.1089/ipm.10.05.12Published in Volume: 10 Issue 5: October 26, 2023PDF download
Integrating Chinese Core Values into Extra-curriculum Activities: A Practice of Themed English Corner

The issue of how to promote the development of core values throughout the curriculum is a frequently mentioned topic that needs further exploration. But few previous studies have investigated the important role extra-curriculum activities play in English learning and education about core values. As such, this paper aims to discuss a feasible path and steps of integrating core values into extra-curriculum activities. Through the analysis on an English corner, it is proposed that the core values should be integrated into the goals of the activity accurately, the content of activity deeply, the process of activity organically, and the assessment of activity naturally, so as to help students’ acquire knowledge, capacities and correct values comprehensively.
Class Representatives Selection in Non-metric Spaces for Nearest Prototype Classification

The nearest prototype classification is a less computationally intensive replacement for the $$k$$ -NN method, especially when large datasets are considered. Centroids are often used as prototypes to represent whole classes in metric spaces. Selection of class prototypes in non-metric spaces is more challenging as the idea of computing centroids is not directly applicable. Instead, a set of representative objects can be used as the class prototype. In this paper, we present CRS, a novel memory and computationally efficient method that finds a small yet representative set of objects from each class to be used as prototype. CRS leverages the similarity graph representation of each class created by the NN-Descent algorithm to pick a low number of representatives that ensure sufficient class coverage. Thanks to the graph-based approach, CRS can be applied to any space where at least a pairwise similarity can be defined. In the experimental evaluation, we demonstrate that our method outperforms the state-of-the-art techniques on multiple datasets from different domains.
PENGEMBANGAN ALAT PERAGA ELEKTRONIKA PADA MATERI RANGKAIAN ARUS BOLAK-BALIK (ALTERNATING CURRENT) SEBAGAI MEDIA PEMBELAJARAN

Abstract: Researchers have conducted research on the development of electronic props in the AC current circuit material. This development research is research that aims to improve existing products. The purpose of this research and development is to determine the level of the feasibility of electronic props on the material of AC current circuits that are developed as learning media and to find out the respondent's response to electronic props on the material of AC current circuits developed. The research method uses Research and Development / R &amp; D with the ADDIE development model which consists of Analysis (analysis), Design (design), Development (development), Implementation (implementation) and Evaluation (evaluation) stages. The analysis phase is to see the condition of the existing equipment. The Design Stage is designing the props that will be developed. The Development Phase is to develop by conducting expert validation. The implementation phase is to do a small group trial and evaluation stage, which is to repair electronic teaching aids in the material of AC current circuits developed. The results of research on electronic props media on the material of alternating current circuits are very feasible. Based on the results of the analysis of the assessment of media experts on electronic props on the material alternating current series obtained an average percentage of 82% with very decent criteria. Assessment of material experts obtained an average percentage of 81% with very reasonable criteria. The results of the student response questionnaire obtained a percentage of 89% with very agree criteria. Based on the results of the discussion regarding the development of electronic props in the AC current circuit material, it can be concluded that the teaching aids developed are very suitable for use as learning media. Suggestion, the use of electronic props on AC current circuit material developed for further research is expected to increase student interest and provide ease of understanding.
Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from a Monocular Camera and IMUs

Automatic evaluating systems are fundamental issues in sports technologies. In many sports, such as figure skating, automated evaluating methods based on pose estimation have been proposed. However, previous studies have evaluated skaters' skills in 2D analysis. In this paper, we propose an automatic edge error judgment system with a monocular smartphone camera and inertial sensors, which enable us to analyze 3D motions. Edge error is one of the most significant scoring items and is challenging to automatically judge due to its 3D motion. The results show that the model using 3D joint position coordinates estimated from the monocular camera as the input feature had the highest accuracy at 83% for unknown skaters' data. We also analyzed the detailed motion analysis for edge error judgment. These results indicate that the monocular camera can be used to judge edge errors automatically. We will provide the figure skating single Lutz jump dataset, including pre-processed videos and labels at https://github.com/ryota-takedalab/JudgeAI-LutzEdge.
COPA : Efficient Vision-Language Pre-training through Collaborative Object- and Patch-Text Alignment

Vision-Language Pre-training (VLP) methods based on object detection enjoy the rich knowledge of fine-grained object-text alignment but at the cost of computationally expensive inference. Recent Visual-Transformer (ViT)-based approaches circumvent this issue while struggling with long visual sequences without detailed cross-modal alignment information. This paper introduces a ViT-based VLP technique that efficiently incorporates object information through a novel patch-text alignment mechanism. Specifically, we convert object-level signals into patch-level ones and devise a Patch-Text Alignment pre-training task (PTA) to learn a text-aware patch detector. By using off-the-shelf delicate object annotations in 5% training images, we jointly train PTA with other conventional VLP objectives in an end-to-end manner, bypassing the high computational cost of object detection and yielding an effective patch detector that accurately detects text-relevant patches, thus considerably reducing patch sequences and accelerating computation within the ViT backbone. Our experiments on a variety of widely-used benchmarks reveal that our method achieves a speedup of nearly 88% compared to prior VLP models while maintaining competitive or superior performance on downstream tasks with similar model size and data scale.
GoRec: A Generative Cold-start Recommendation Framework

Multimedia-based recommendation models learn user and item preference representation by fusing both the user-item collaborative signals and the multimedia content signals. In real scenarios, cold items appear in the test stage without any user interaction record. How to perform cold item recommendation is challenging as the training items and test items have different data distributions. These hybrid preference representations contained auxiliary collaborative signals, so current solutions designed alignment functions to transfer learned hybrid preference representations to cold items. Despite the effectiveness, we argue that they are still limited as these models relied heavily on the manually carefully designed alignment functions, which are easily influenced by the limited item records and noises in the training data.
Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examples

Safeguarding data from unauthorized exploitation is vital for privacy and security, especially in recent rampant research in security breach such as adversarial/membership attacks. To this end,unlearnable examples (UEs) have been recently proposed as a compelling protection, by adding imperceptible perturbation to data so that models trained on them cannot classify them accurately on original clean distribution. Unfortunately, we find UEs provide a false sense of security, because they cannot stop unauthorized users from utilizing other unprotected data to remove the protection, by turning unlearnable data into learnable again. Motivated by this observation, we formally define a new threat by introducinglearnable unauthorized examples (LEs) which are UEs with their protection removed. The core of this approach is a novel purification process that projects UEs onto the manifold of LEs. This is realized by a new joint-conditional diffusion model which denoises UEs conditioned on the pixel and perceptual similarity between UEs and LEs. Extensive experiments demonstrate that LE delivers state-of-the-art countering performance against both supervised UEs and unsupervised UEs in various scenarios, which is the first generalizable countermeasure to UEs across supervised learning and unsupervised learning. Our code is available at https://github.com/jiangw-0/LE_JCDP.
Automatic Asymmetric Embedding Cost Learning via Generative Adversarial Networks

In comparison to symmetric embedding, asymmetric methods generally provide better steganography security. However, the performance of existing asymmetric methods is limited by their reliance on symmetric embedding costs. In this paper, we present a novel Generative Adversarial Network (GAN)-based steganography approach that independently learns asymmetric embedding costs from scratch. Our proposed framework features a generator with a dual-branch architecture and a discriminator that integrates multiple steganalytic networks. To address the issues of model instability and non-convergence that often arise in GAN model training, we implement an adaptive strategy that updates the GAN model parameters according to the performance of multiple steganalytic networks in each iteration. Furthermore, we introduce a new adversarial loss function that effectively learns asymmetric embedding costs by utilizing features like image residuals, gradients, asymmetric embedding probability maps, and the sign of the modification map to train the dual-branch network within the generator. Our comprehensive experiments show that our method achieves state-of-the-art steganography security results, significantly outperforming existing top-performing symmetric and asymmetric methods. Additionally, numerous ablation experiments confirm the rationality of our GAN-based model design.
The ACM Multimedia 2023 Deep Video Understanding Grand Challenge

This is the overview paper for the Deep Video Understanding (DVU) Grand Challenge. In recent years, a growing trend towards working on understanding videos (in particular movies) to a deeper level started to motivate researchers working in multimedia and computer vision to present new approaches and datasets to tackle this problem. This is a challenging research area which aims to develop a deep understanding of the relations which exist between different individuals and entities in movies using all available modalities such as video, audio, text and metadata. The aim of this grand challenge is to foster innovative research in this new direction and to provide benchmarking evaluations to advance technologies in the deep video understanding community.
Sliding Window Seq2seq Modeling for Engagement Estimation

Engagement estimation in human conversations has been one of the most important research issues for natural human-robot interaction. However, previous datasets and studies mainly focus on the video-wise level of engagement estimation, therefore, can hardly reflect human's constantly changing engagement. Fortunately, the MultiMediate '23 challenge provides the frame-wise level of engagement estimation task. In this paper, we propose Sliding Window Seq2seq Modeling by BiLSTM and Transformer with powerful sequence modeling capabilities. Our method fully utilizes the global and local multi-modal feature information in the participants' videos and accurately expresses the engagement of the participants at each moment. Our method achieves the state-of-the-art CCC result of 0.71 for engagement estimation on the corresponding test sets.