Reviewerly/exHarmony huggingface dataset:
Updates to the R Graphics Engine: One Person's Chart Junk is Another's Chart Treasure

Starting from R version 4.1.0, the R graphics engine has gained support for gradient fills, pattern fills, clipping paths, masks, compositing operators, and stroked and filled paths. This document provides a basic introduction to each of these new features and demonstrates how to use the new features in R.
Dynamics of Age Characteristics of the Teaching Staff of the Higher School: Current Trends and Risk Assessment

The personnel renewal of the teaching staff of higher education (teaching staff) is considered one of the factors contributing to the development of the domestic education system, which implies an increase in the proportion of scientific and pedagogical staff of universities under the age of 35 according to the national project "Education" to 20%. The subject of the study is the dynamics of the structural characteristics of the PPP. The purpose of the work is to identify the main trends in the age dynamics of teaching staff. The study found that the age characteristics of teaching staff cannot fully serve as indicators of the maturity of university teachers to solve professional tasks and educate harmoniously developed and socially responsible individuals based on the spiritual and moral values of the peoples of the Russian Federation, although the process of "aging" teaching staff causes a number of risks to the higher education system due to age bias priorities of teachers in the professional qualification plan. The ambiguous assessment by the educational community of the tasks associated with the "rejuvenation" of the teaching staff, in the absence of optimal mechanisms of management activities to adjust the age structure of teachers in universities, is demonstrated. It is concluded that it is necessary to develop and implement a strategically sound personnel policy, within the framework of which there is no formal replacement of older teachers with younger staff, but systematic work is implemented based on compliance with the following principles: flexibility of professional trajectories, differentiated nature of support measures, transparency of competition criteria, inadmissibility of discrimination.
A Niching Ring Topology Genetic Algorithm for Multimodal Optimization

Most real-world problems exhibit a multimodal property, meaning that there are several global or local optimal solutions present in the landscape of the function. Recent research has paid significant attention to multimodal optimization. This paper presents a novel niching Genetic Algorithm (GA) that is designed for multimodal optimization. Our approach involves the formation of niches based on individual positions, and the genetic operators are formulated to avoid disrupting these niches. Experimental analysis confirms that the proposed method outperforms previously introduced methods.
Early childhood teachers’ perceptions and difficulties of play-based science education

This research aims to analyze the perceptions and challenges faced by early childhood teachers regarding play-based science education and seeks to explore implications and strategies for enhancing science education. To achieve these goals, the study conducted a collaborative learning community with 11 teachers (5 for toddlers and 6 for kindergartners) working in 4 childcare centers in A City. The researchers collected scientific activity videos, educational materials, childcare records, teachers’ journals, and other data from the participating teachers. Using a thematic coding analysis method, the study categorized the perceptions of early childhood teachers regarding science education into three main themes: 'Understanding play-based science education,' 'Science education with the goal of knowledge transmission,' and 'Challenges and difficulties in science education.' Furthermore, in the field of early childhood education, teachers were found to recognize difficulties in implementing play-based science education. These difficulties included challenges related to "Finding and supporting science within play," "Psychological and physical challenges faced by teachers," and "Challenges with principals, other teachers, and parents." The significance of this study lies in its potential to serve as foundational material for understanding teachers' perceptions of science education and addressing the challenges they face in teaching science.
Plans to Expand and Revitalize Daycare Centers for 0-1-Year-Old Children in Gyeonggi-do

The purpose of this study is to study the direction of the development of daycare centers as a way to expand and revitalize the 0 to 1-year-old childcare center project, which is a specialized childcare project in Gyeonggi-do. The 0-year-old childcare center in Gyeonggi-do is the first childcare facility for 0 to 1-year-olds in the country in 2008 so that dual-income families can trust and entrust their children. In-depth interviews were conducted to study ways to expand and revitalize the specialized childcare projects in Gyeonggi-do, and the study participants consisted of four directors and three teachers working at a daycare center for 0 to 1-year-olds in Gyeonggi-do. In the process of searching for data, the ratio of teachers to children, the demand for specialized infant curriculum, the concerns over the integration of kindergartens and daycare centers, the director's concurrent position as teachers, and the demand for improvement of the childcare environment were found. Based on the demand for improvement, the policy proposals for the future development direction of daycare centers are as follows. First, support for the mandatory infant professional curriculum and in-depth curriculum for the professional reinforcement education of 0 to 1-year-old teachers was derived as an alternative. Second, the need for a review since 2015 was mentioned as a discussion on the director's concurrent position as a teacher, and the need to seriously consider whether the clarification of guidelines and supervision are well followed and effective was proposed. Third, we proposed to improve the environment for professional infant care by improving the childcare environment, secure the expertise of cooks, and devise measures to resolve the difficulty of preparing infant textbooks and parish play. Fourth, we proposed to provide services for customized care at the birth registration stage through active promotion of daycare centers for children aged 0 years. Based on this, we hope that the daycare center for children aged 0 to 1in Gyeonggi-do will be expanded and revitalized to start discussions to develop.
Strategi Perpustakaan Dalam Mendukung Pembelajaran Secara Daring di Universitas Islam Negeri Raden Intan Lampung

Circular No.36962/MPK.A/HK/2020 it was decided that online learning and working from home are for breaking the chain of the spread of covid19. The application of the online teaching and learning system is a means of proving the professional competence of librarians as a link of information between information sources and information users. The library law number 43 of 2007 states that a librarian is someone who has a competence. Based on several problems that exist in the online learning system in universities, especially at the Raden Intan State Islamic University Lampung (UIN-RIL), this paper raises a problem formulation, namely How Library Strategies Support Online Learning at Raden Intan State Islamic University Lampung. The method used in this research is qualitative with qualitative descriptive type. This study also reveals attitudes, events, relationships and views that occur in a scope of respondents. This study places more emphasis on the results. This study uses observation and interviews to strengthen the research analysis. Discussion of Library Service Transformation consists of Online Catalog, Circulation thru Service and Institutional Repository, Digital Promotion Information Communication and Mobile Library. Success in learning during a pandemic cannot be separated from the role of libraries and librarians in preparing strategically in providing library collection information or alternative sources of information.
Optical Sensor Implementation for Health Care Monitoring Based on Optical Fiber Technology: A Review

This study provides a review of the literature and an overview of optical fibers, as it is considered the highest class of intelligent materials, and there are many varieties and divisions based on the requirements of the manufacturer, it will review Optic Fiber Sensors (OFS) for measuring temperature and humidity. It has been divided into four main sections: The end user, and the environment. OFS Application in Healthcare, Impact OFS Qatar, Effect of chemical and mechanical properties of OFS Effect of external temperature changes in Surface Plasmon Resonance (SPR) for OFS Due to its unique characteristics, such as small size, lack of electromagnetic interference, high sensitivity, weights that allow sensing, accuracy, and very high dynamic range, it generates a fiber-optic sensor that is easy to manufacture and operate at the lowest possible cost. In addition, the development of Dispersive Sensor Systems (DSS) and OFS has found applications in both healthcare settings and strategies in biomedical research and structural Health Care Monitoring (HCM) to enhance the adoption of fiber-based medical equipment. Optics are two areas in which OFS has yet to realize its huge potential fully. The most recent published studies (2010-2023) were our main focus except for a few cases.
Study of Low-Altitude Emergency Response Alternative Selection Process

To address the complexity of the departmental coordination with others effectively, an approach for selecting emergency alternatives based on multi-granularity linguistic and multidivisional cooperation was presented. Firstly, multi-granularity linguistic phrases were employed to express the preference information, and some transformation functions were used to unify the multi-granular linguistic phrases into a uniform linguistic label set. Secondly, the evaluation indexes of key attributes with respect to each combination of alternations were determined considering multidivisional cooperation. Furthermore, according to the evaluation indexes and the weight vector of key attributes, the comprehensive value of each combination alternative was determined to obtain the best alternative. Finally, a case study of low-attitude airspace emergency rescue after an earthquake is presented to illustrate the validity of the approach.
HoloFusion: Towards Photo-realistic 3D Generative Modeling

Diffusion-based image generators can now produce high-quality and diverse samples, but their success has yet to fully translate to 3D generation: existing diffusion methods can either generate low-resolution but 3D consistent outputs, or detailed 2D views of 3D objects but with potential structural defects and lacking view consistency or realism. We present HoloFusion, a method that combines the best of these approaches to produce high-fidelity, plausible, and diverse 3D samples while learning from a collection of multi-view 2D images only. The method first generates coarse 3D samples using a variant of the recently proposed HoloDiffusion generator. Then, it independently renders and upsamples a large number of views of the coarse 3D model, super-resolves them to add detail, and distills those into a single, high-fidelity implicit 3D representation, which also ensures view-consistency of the final renders. The super-resolution network is trained as an integral part of HoloFusion, end-to-end, and the final distillation uses a new sampling scheme to capture the space of super-resolved signals. We compare our method against existing baselines, including DreamFusion, Get3D, EG3D, and HoloDiffusion, and achieve, to the best of our knowledge, the most realistic results on the challenging CO3Dv2 dataset.
Normalizing Flows for Human Pose Anomaly Detection

Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more. We distill the problem to anomaly detection of human pose, thus decreasing the risk of nuisance parameters such as appearance affecting the result. Focusing on pose alone also has the side benefit of reducing bias against distinct minority groups.Our model works directly on human pose graph sequences and is exceptionally lightweight (~1K parameters), capable of running on any machine able to run the pose estimation with negligible additional resources. We leverage the highly compact pose representation in a normalizing flows framework, which we extend to tackle the unique characteristics of spatio-temporal pose data and show its advantages in this use case.The algorithm is quite general and can handle training data of only normal examples as well as a supervised setting that consists of labeled normal and abnormal examples.We report state-of-the-art results on two anomaly detection benchmarks - the unsupervised ShanghaiTech dataset and the recent supervised UBnormal dataset. Code available at https://github.com/orhir/STG-NF.
Masked Autoencoders Are Stronger Knowledge Distillers

Knowledge distillation (KD) has shown great success in improving student’s performance by mimicking the intermediate output of the high-capacity teacher in fine-grained visual tasks, e.g. object detection. This paper proposes a technique called Masked Knowledge Distillation (MKD) that enhances this process using a masked autoencoding scheme. In MKD, random patches of the input image are masked, and the corresponding missing feature is recovered by forcing it to imitate the output of the teacher. MKD is based on two core designs. First, using the student as the encoder, we develop an adaptive decoder architecture, which includes a spatial alignment module that operates on the multi-scale features in the feature pyramid network (FPN) [20], a simple decoder, and a spatial recovery module that mimics the teacher’s output from the latent representation and mask tokens. Second, we introduce the masked convolution in each convolution block to keep the masked patches unaffected by others. By coupling these two designs, we can further improve the completeness and effectiveness of teacher knowledge learning. We conduct extensive experiments on different architectures with object detection and semantic segmentation. The results show that all the students can achieve further improvements compared to the conventional KD. Notably, we establish the new state-of-the-art results by boosting RetinaNet ResNet-18, and ResNet-50 from 33.4 to 37.5 mAP, and 37.4 to 41.5 mAP, respectively.
A Theory of Topological Derivatives for Inverse Rendering of Geometry

We introduce a theoretical framework for differentiable surface evolution that allows discrete topology changes through the use of topological derivatives for variational optimization of image functionals. While prior methods for inverse rendering of geometry rely on silhouette gradients for topology changes, such signals are sparse. In contrast, our theory derives topological derivatives that relate the introduction of vanishing holes and phases to changes in image intensity. As a result, we enable differentiable shape perturbations in the form of hole or phase nucleation. We validate the proposed theory with optimization of closed curves in 2D and surfaces in 3D to lend insights into limitations of current methods and enable improved applications such as image vectorization, vector-graphics generation from text prompts, single-image reconstruction of shape ambigrams and multiview 3D reconstruction.
Scene-Aware Label Graph Learning for Multi-Label Image Classification

Multi-label image classification refers to assigning a set of labels for an image. One of the main challenges of this task is how to effectively capture the correlation among labels. Existing studies on this issue mostly rely on the statistical label co-occurrence or semantic similarity of labels. However, an important fact is ignored that the co-occurrence of labels is closely related with image scenes (indoor, outdoor, etc.), which is a vital characteristic in multi-label image classification. In this paper, a novel scene-aware label graph learning framework is proposed, which is capable of learning visual representations for labels while fully perceiving their co-occurrence relationships under variable scenes. Specifically, our framework is able to detect scene categories of images without relying on manual annotations, and keeps track of the co-occurring labels by maintaining a global co-occurrence matrix for each scene category throughout the whole training phase. These scene-independent co-occurrence matrices are further employed to guide the interactions among label representations in a graph propagation manner towards accurate label prediction. Extensive experiments on public benchmarks demonstrate the superiority of our framework.
Ablating Concepts in Text-to-Image Diffusion Models

Large-scale text-to-image diffusion models can generate high-fidelity images with powerful compositional ability. However, these models are typically trained on an enormous amount of Internet data, often containing copyrighted material, licensed images, and personal photos. Furthermore, they have been found to replicate the style of various living artists or memorize exact training samples. How can we remove such copyrighted concepts or images without retraining the model from scratch? To achieve this goal, we propose an efficient method of ablating concepts in the pretrained model, i.e., preventing the generation of a target concept. Our algorithm learns to match the image distribution for a target style, instance, or text prompt we wish to ablate to the distribution corresponding to an anchor concept. This prevents the model from generating target concepts given its text condition. Extensive experiments show that our method can successfully prevent the generation of the ablated concept while preserving closely related concepts in the model.
PoseFix: Correcting 3D Human Poses with Natural Language

Automatically producing instructions to modify one’s posture could open the door to endless applications, such as personalized coaching and in-home physical therapy. Tackling the reverse problem (i.e., refining a 3D pose based on some natural language feedback) could help for assisted 3D character animation or robot teaching, for instance. Although a few recent works explore the connections between natural language and 3D human pose, none focus on describing 3D body pose differences. In this paper, we tackle the problem of correcting 3D human poses with natural language. To this end, we introduce the Pose- Fix dataset, which consists of several thousand paired 3D poses and their corresponding text feedback, that describe how the source pose needs to be modified to obtain the target pose. We demonstrate the potential of this dataset on two tasks: (1) text-based pose editing, that aims at generating corrected 3D body poses given a query pose and a text modifier; and (2) correctional text generation, where instructions are generated based on the differences between two body poses. The dataset and the code are available at https://europe.naverlabs.com/research/computer-vision/posefix/.
RECURSPIVE SEPARABLE 2D DIGITAL FILTER FOR INCREASING THE SHARPNESS OF RGB IMAGES

An important role in the perception of image quality is sharpness, that is, the An importantrole in the perception of image quality is played by sharpness, that is, the magnitude of the brightnessgradient in areas near the boundaries of objects. This characteristic is responsible for theclarity and detail of small image elements. Defocusing the camera lens and insufficient illuminationare the main factors that can lead to digital image blurring. To increase the sharpness, variousprocessing methods are used, such as filtering in the frequency domain, for example, the use offast Fourier transform to emphasize the boundaries and textures of the image. The use of this typeof filtering allows you to control the contrast and frequency content of the image, which leads toan improvement in visual perception. However, this method has a number of significant drawbacks,such as logarithmic complexity and performing additional calculations associated withforward and inverse Fourier transforms. Therefore, the preferred method of image sharpening isthe so-called spatial processing, which provides direct filtering of image pixels without additionaltransformations, and the reuse of processing results (recursive component) in the filter allows youto reduce the number of operations, reduce computational complexity. The article describes thedevelopment of an effective recursive separable two-dimensional digital filter to sharpen largedimensionalRGB images. The algorithms of its construction are given, the corresponding blockdiagrams are designed. The filter has the property of more uniform detail of image objects, and isless susceptible to the creation of pulse noise. Also, for the original high-resolution RGB image, ablur filter is modeled, the matrix of which is filled according to the normal (Gaussian) law.To assess the filtration quality, the developed filter is compared with the algorithm of classicaltwo-dimensional convolution with a 5x5 Laplace high-pass filter core.
Peran Badan Penanggulangan Bencana Daerah (BPBD) Kota Yogyakarta dalam Penanggulangan Bencana Banjir di Kelurahan Pandeyan Kemantren Umbulharjo Kota Yogyakarta

Almost every year, Indonesia experiences flood disasters, such as those experienced in Pandeyan Village. Thus, Pandeyan Subdistrict needs the role of Yogyakarta City BPBD in flood disaster management efforts. This research aims to determine and analyze the role of the Yogyakarta City BPBD in managing flood disasters in Pandeyan Village, Kemantren Umbulharjo, Yogyakarta City. This research uses descriptive qualitative research and uses data collection techniques such as observation, interviews and documentation. The data analysis techniques used are data reduction, data presentation, and drawing conclusions. The research results show that the role of Yogyakarta City BPBD in flood disaster management has carried out and fulfilled its role in accordance with the role theory put forward by Sondang P. Siagian. Yogyakarta City BPBD has played a role in flood disaster management efforts through several programs and activities carried out in Pandeyan Village
Evaluasi Aspek Teknis Kualitas Air Membran Ultrafiltrasi dalam Pengolahan Air Minum (Studi Kasus: IPA Krian Perumda Air Minum Sidoarjo)

The Sidoarjo Regional Public Company (Perumda) is a regionally owned business entity that participates in managing and providing clean water which will be distributed to the people of Sidoarjo Regency. The Sidoarjo Drinking Water Company, especially the Krian IPA, uses the main channel to supply raw water from the Mangetan Canal which passes through seven sluice gates to irrigate 17,583 Ha of rice fields. The series of air treatment installations at Perumda Air Minum Sidoarjo are coagulation units, flocculation units, sedimentation units and ultrafiltration membranes. However, not all technology can be implemented optimally due to considerations of costs and Perumda personnel. The problem is that the ultrafiltration membrane unit at Perumda Air Minum Sidoarjo is not activated. Therefore, the aim of the research is to determine the ineffectiveness of ultrafiltration membranes at Perumda Air Minum Sidoarjo in terms of technical aspects, namely the quality of water resulting from membrane processing in 2016-2020. The results of the research on the technical aspect, namely data on water quality parameters for turbidity and color for 2016-2020, have complied with Minister of Health Regulation Number 2 of 2023 for drinking water quality standards, the rejection percentage for turbidity parameters was 69.39%. - 95.48%; the percentage of color parameter rejection was 43.60% - 61.6% with the resulting flux value decreasing.