Reviewerly/exHarmony huggingface dataset:
Analysis on Education of Sustainable Development and Presentation of Textbook Development Direction based on the 2022 Revised Curriculum: Focusing on 5th to 6th graders in Elementary school

Objectives This study analyzes the 2022 revised National Curriculum Framework (draft) and its alignment with Education for Sustainable Development(ESD) to derive implications for textbook development.&#x0D; Methods The research examines the subject-specific objectives, content systems, and achievement standards in terms of their relevance to the core content and elements of ESD. The study's objective is implemented by analyzing the content of the National Curriculum Framework(draft), subject-specific objectives, content systems, and achievement standards for each subject, while selecting relevant elements of ESD based on previous research.&#x0D; Results Firstly, the National Curriculum Framework(draft) is evaluated in relation to the concept of sustainable development, encompassing social, environmental, and economic perspectives of ESD. The analysis reveals that seven out of the nine social perspectives are reflected, while only four out of the nine environmental perspectives and one out of the four economic perspectives are included. This highlights the need for a more balanced approach across all perspectives. Secondly, all subjects except mathematics incorporate elements of ESD in their subject-specific objectives for fifth and sixth grades. However, apart from some environmental perspectives found in ethics, society, and practical arts, the objectives predominantly align with the social perspective. Achievement standards demonstrate a higher inclusion of core contents and content elements of sustainable development education compared to objectives and content systems. Lastly, textbook development based on the 2022 revised curriculum should ensure equal representation of all three perspectives of ESD, with diverse content elements included within each perspective. It is crucial to incorporate ESD into all subjects.&#x0D; Conclusions The study emphasizes the growing demand for ESD to prepare for future societies. The findings serve as fundamental guidelines for the application of ESD in textbook development and contribute to the creation of desirable textbooks.
Pesquisa qualitativa e quantitativa sobre o design de chatbots no mercado brasileiro

The aim of this paper was to investigate the challenges, needs, and design processes of chatbots, also known as conversational agents or chat robots, in the Brazilian market.To achieve this, a review of available literature on related topics, such as the conceptualization and construction of chatbots, as well as definitions of UX Writing, was conducted.Subsequently, a quantitative survey was conducted to understand who the professionals working in this market are and what their main difficulties are, followed by four interviews to understand the chatbot design processes of these professionals.As a result, the difficulty in defining the scope of job positions of chatbot designers, the limited use of design methodologies and tools, and the scarcity of theoretical knowledge in the field were observed.Based on these findings, it is possible to guide academic production for chatbot design and develop better practices related to the field.
Pelatihan Teknik Parafrase Karya Ilmiah Pustakawan Perguruan Tinggi Di Pekanbaru

Plagiarism is an issue that gets serious attention in academia. Disclosure of information is one of the causes of plagiarism. There must be an effort to prevent it, one of which is paraphrasing scientific papers. Based on the results of observations of partners, it was found that several problems were experienced by partners. These problems include: a) the habit of copy-pasting that is still carried out by librarians in compiling scientific papers, b) not having complete knowledge about plagiarism and the sanctions incurred. The method used is to provide training to university librarians. The results show that all targets have been well achieved in accordance with the proposed program. The training activities received a positive response from partners and participants. This can be seen from the level of enthusiasm of the participants in participating in the training. From the aspect of understanding and skills of participants, there was an increase in understanding of paraphrasing and skills in using the Sipebi application. it can be seen in the indicators of the results of the pretest and posttest. Meanwhile, the output of this activity is scientific articles published in national journals.
Epidemiological profile of patients with systemic lupus erythematosus assisted at Hospital Getúlio Vargas

Objective: To trace the epidemiological profile of patients with SLE treated at Hospital Getúlio Vargas (HGV), in Teresina, Piauí. Methods: Descriptive and retrospective study, whose data were collected by consulting medical records in the Internal Medicine sector of the hospital. Results: Sixty-six patients with SLE, mean age 38.65±15.02 years, were included. There was a higher prevalence of female individuals (89.4%), aged between 18 and 30 years (39.4%), being single (31.8%) or married (31.8%), coming from the interior of the state (56.0%), brown color/race (53.0%). About 69.7% reported having no family history of diseases and the most reported comorbidities were: Cushing's Syndrome (18.2%), Osteoporosis (16.7%), Hypertension (15.2%), Lupus Nephritis (12 .1%), Hypothyroidism (10.6%) and Diabetes mellitus (9.1%). Most cases were diagnosed in 2021 (19.7%) and had lived with the disease for 1 to 5 years (48.5%). Conclusion: The analysis of patients with SLE treated at Hospital Getúlio Vargas showed that the number of patients diagnosed with the disease has been growing over the years in the state of Piauí, considering that 2021 was the year with the highest number of diagnoses.
Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding

The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state. We also highlight the demand for imagining regretful explorations with semantically meaningful clues. The key to our approach is understanding the object placements around the agent in spectral-domain. Specifically, we present a novel visual representation, called scene object spectrum (SOS), which performs category-wise 2D Fourier transform of detected objects. Combining exploitation policy and SOS features, the agent can correct its path by choosing a promising local goal. We evaluate our method in three VLN benchmarks: R2R, SOON, and REVERIE. Meta-Explore outper-forms other baselines and shows significant generalization performance. In addition, local goal search using the proposed spectral-domain SOS features significantly improves the success rate by 17.1% and SPL by 20. 6% against the state-of-the-art method of the SOON benchmark. Project page: https://rllab-snu.github.io/projects/Meta-Explore/doc.html
Train-Once-for-All Personalization

We study the problem of how to train a “personalization-friendly” model such that given only the task descriptions, the model can be adapted to different end-users' needs, e.g., for accurately classifying different subsets of objects. One baseline approach is to train a “generic” model for classifying a wide range of objects, followed by class selection. In our experiments, we however found it suboptimal, perhaps because the model's weights are kept frozen without being personalized. To address this drawback, we propose Train-once-for-All PERsonalization (TAPER), a framework that is trained just once and can later customize a model for different end-users given their task descriptions. TAPER learns a set of “basis” models and a mixer predictor, such that given the task description, the weights (not the predictions!) of the basis models can be on the fly combined into a single “personalized” model. Via extensive experiments on multiple recognition tasks, we show that TAPER consistently outperforms the baseline methods in achieving a higher personalized accuracy. Moreover, we show that TAPER can synthesize a much smaller model to achieve comparable performance to a huge generic model, making it “deployment-friendly” to resource-limited end devices. Interestingly, even without end-users' task descriptions, TAPER can still be specialized to the deployed context based on its past predictions, making it even more “personalization-friendly”.
CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation

Existing semantic segmentation approaches are often limited by costly pixel-wise annotations and predefined classes. In this work, we present CLIP-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> that leverages self-supervised pixel representation learning and vision-language models to enable various semantic segmentation tasks (e.g., unsupervised, transfer learning, language-driven segmentation) without any human annotations and unknown class information. We first learn pixel embeddings with pixel-segment contrastive learning from different augmented views of images. To further improve the pixel embeddings and enable language-driven semantic segmentation, we design two types of consistency guided by vision-language models: 1) embedding consistency, aligning our pixel embeddings to the joint feature space of a pre-trained vision-language model, CLIP [34]; and 2) semantic consistency, forcing our model to make the same predictions as CLIP over a set of carefully designed target classes with both known and unknown prototypes. Thus, CLIP-S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> enables a new task of class-free semantic segmentation where no unknown class information is needed during training. As a result, our approach shows consistent and substantial performance improvement over four popular benchmarks compared with the state-of-the-art unsupervised and language-driven semantic segmentation methods. More importantly, our method outperforms these methods on unknown class recognition by a large margin.
Hybrid Neural Rendering for Large-Scale Scenes with Motion Blur

Rendering novel view images is highly desirable for many applications. Despite recent progress, it remains challenging to render high-fidelity and view-consistent novel views of large-scale scenes from in-the-wild images with inevitable artifacts (e.g., motion blur). To this end, we develop a hybrid neural rendering model that makes image-based representation and neural 3D representation join forces to render high-quality, view-consistent images. Besides, images captured in the wild inevitably contain artifacts, such as motion blur, which deteriorates the quality of rendered images. Accordingly, we propose strategies to simulate blur effects on the rendered images to mitigate the negative influence of blurriness images and reduce their importance during training based on precomputed quality-aware weights. Extensive experiments on real and synthetic data demonstrate our model surpasses state-of-the-art point-based methods for novel view synthesis. The code is available at https://daipengwa.github.io/Hybrid-Rendering-ProjectPage/.
Contrastive Knowledge Distillation for Anomaly Detection in Multi-Illumination/Focus Display Images

In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus display images. The minute defects on the display surface are hard to spot out in RGB images and by a model trained with only normal data. To address this, we propose a novel contrastive learning scheme for knowledge distillation-based anomaly detection. In our framework, Multiresolution Knowledge Distillation (MKD) is adopted as a baseline, which operates by measuring feature similarities between the teacher and student networks. Based on MKD, we propose a novel contrastive learning method, namely Multiresolution Contrastive Distillation (MCD), which does not require positive/negative pairs with an anchor but operates by pulling/pushing the distance between the teacher and student features. Furthermore, we propose the blending module that transforms and aggregate multi-channel information to the three-channel input layer of MCD. Our proposed method significantly outperforms competitive state-of-the-art methods in both AUROC and accuracy metrics on the collected Multi-illumination and Multi-focus display image dataset for Anomaly Detection (MMdAD).
Leveraging Machine Learning for Enhanced Video Quality in E-Learning

In the rapidly evolving landscape of online education, video content has become an indispensable tool for effective knowledge dissemination. However, ensuring high- quality video content remains a challenge due to various factors such as bandwidth limitations, device diversity, and encoding constraints. This paper proposes a Multi-Frame Super- Resolution approach to address this challenge by leveraging machine learning techniques to enhance video quality in e-learning environments.Traditional video enhancement techniques often rely on predefined filters or manual adjustments, which can lead to inconsistent results and significant time investments. In contrast, the proposed approach utilizes machine learning algorithms to automatically analyze and improve video quality. By training on a diverse dataset of e- learning videos, the model learns to identify and correct common issues such as compression artifacts, low resolution, and visual noise.The proposed machine learning- based approach offers a scalable and automated solution to enhance video quality in e-learning without requiring manual intervention for each video. As online education continues to grow, the integration of such technology holds the potential to elevate the overall quality of e-learning experiences and improve knowledge retention. Key Words: E-learning, video quality enhancement, machine learning, convolutional neural networks, objective metrics.
RFID Backscatter Based Sport Motion Sensing Using ECOC-Based SVM

With the advent of the 5G era, radio frequency identification (RFID) has been widely applied in various fields as one of the key technologies for the Internet of Things (IoT) to realize the Internet of Everything (IoE). In recent years, RFID-based motion sensing has emerged as an important research area with great potential for development. In this paper, an RFID backscatter sport motion sensing scheme is proposed, which effectively solves the multi-classification problem by using the received signal strength (RSS) of the backscattered RFID and the error correcting output coding (ECOC)-based support vector machine (SVM). We conduct extensive experiments to validate the effectiveness of the proposed scheme, in which the signal intensities of different types of action poses are collected and the SVM is used as the classification algorithm to achieve high classification accuracies.
Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos

Video semantic segmentation (VSS) is a computationally expensive task due to the per-frame prediction for videos of high frame rates. In recent work, compact models or adaptive network strategies have been proposed for efficient VSS. However, they did not consider a crucial factor that affects the computational cost from the input side: the input resolution. In this paper, we propose an altering resolution framework called AR-Seg for compressed videos to achieve efficient VSS. AR-Seg aims to reduce the computational cost by using low resolution for non-keyframes. To prevent the performance degradation caused by downsampling, we design a Cross Resolution Feature Fusion (CR-eFF) module, and supervise it with a novel Feature Similarity Training (FST) strategy. Specifically, CReFF first makes use of motion vectors stored in a compressed video to warp features from high-resolution keyframes to low-resolution non-keyframes for better spatial alignment, and then selectively aggregates the warped features with local attention mechanism. Furthermore, the proposed FST supervises the aggregated features with high-resolution features through an explicit similarity loss and an implicit constraint from the shared decoding layer. Extensive experiments on CamVid and Cityscapes show that AR-Seg achieves state-of-the-art performance and is compatible with different segmentation backbones. On CamVid, AR-Seg saves 67% computational cost (measured in GFLOPs) with the PSPNet18 back-bone while maintaining high segmentation accuracy. Code: https://github.com/THU-LYJ-Lab/AR-Seg.
MISC210K: A Large-Scale Dataset for Multi-Instance Semantic Correspondence

Semantic correspondence have built up a new way for object recognition. However current single-object matching schema can be hard for discovering commonalities for a category and far from the real-world recognition tasks. To fill this gap, we design the multi-instance semantic correspondence task which aims at constructing the correspondence between multiple objects in an image pair. To support this task, we build a multi-instance semantic correspondence (MISC) dataset from COCO Detection 2017 task called MISC210K. We construct our dataset as three steps: (1) category selection and data cleaning; (2) keypoint design based on 3D models and object description rules; (3) human-machine collaborative annotation. Following these steps, we select 34 classes of objects with 4,812 challenging images annotated via a well designed semi-automatic workflow, and finally acquire 218,179 image pairs with instance masks and instance-level keypoint pairs annotated. We design a dual-path collaborative learning pipeline to train instance-level co-segmentation task and fine-grained level correspondence task together. Benchmark evaluation and further ablation results with detailed analysis are provided with three future directions proposed. Our project is available on https://github.com/YXSUNMADMAX/MISC210K.
From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm

Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made, such node interaction paradigms still have the following limitation. First, the scalability limitation precludes the broad application of GNNs in large-scale industrial settings since the node interaction among rapidly expanding neighbors incurs high computation and memory costs. Second, the over-smoothing problem restricts the discrimination ability of nodes, i.e., node representations of different classes will converge to indistinguishable after repeated node interactions. In this work, we propose a novel hop interaction paradigm to address these limitations simultaneously. The core idea is to convert the interaction target among nodes to pre-processed multi-hop features inside each node. We design a simple yet effective HopGNN framework that can easily utilize existing GNNs to achieve hop interaction. Furthermore, we propose a multi-task learning strategy with a self-supervised learning objective to enhance HopGNN. We conduct extensive experiments on 12 benchmark datasets in a wide range of domains, scales, and smoothness of graphs. Experimental results show that our methods achieve superior performance while maintaining high scalability and efficiency. The code is at https://github.com/JC-202/HopGNN.
A Prediction Algorithm For Fall Risk Assessment Among Community-Dwelling Elderly People

Worldwide, life expectancy has steadily increased over the years.However, the integrity and the functionality of physiological systems reduce over time making older people more vulnerable to adverse events such as falls.Consequences of falls can include physical injuries (hip fractures), psychological issues (fear of falling) and social isolation (need for assistance) increasing the demand for healthcare services (hospitalization, rehabilitation, institutionalization).Despite many studies investigated the most predisposing factors to a fall risk, this study aimed at identifying those factors through a multidimensional health assessment of elderly people crossing over multiple health domains such as nutritional, clinical, psychological, social, and functional.The acquired variables were processed in terms of data cleaning and coding, to make them ready for subsequent statistical evaluation.Correlation and regression analyses were performed to find out the relevance of the variables with respect to the fall event.The most significant variables were accounted for the development of the predictive index.Each predictor was associated with a score according to specific weights so that the sum of the answers to each question of the index gave the final fall risk index.Its validation was assessed over the sample of the study by comparing the index output to that of the multidimensional evaluation and the one of a common fall risk test.Clinicians will benefit from this tool for a fast and easy screening of fall risk among community-dwelling seniors to act promptly on those subjects at increased risk, and preventively on low-risk subjects.This way, it is possible to optimize time, costs, and resources for sustainable and effective management of patient care.
Automatically Enriching Content for a Behavioral Health Learning Management System: a First Look

Deep generative AI models have been evolving rapidly and are being applied to assist users in many domains.We consider an initial case study of applying this technology to semi-automated content enrichment for an online learning management system used by behavioral health professionals.Our goal is to optimize the use of domain expertise while also scaling the production efficiency of learning assets for users.We identify the possible opportunities for its use, discuss potential challenges and concerns.Finally, we provide prompt engineering strategies and initial quantitative results towards semi-automating one type of rime consuming editorial task, to gauge the feasibility of our approach.Results show that there is significant promise in using such an approach, and suggest that a larger, more rigorous study is required.
Constructing Quadratic and Cubic Negabent Functions over Finite Fields

Bent functions have flat absolute Walsh-Hadamard spectra and negabent functions have flat absolute nega-Hadamard spectra. Those properties are wide applications in cryptography for constructing cryptographically strong functions and error correcting codes for better performance. In this paper, we present a new construction of quadratic and cubic negabent functions over finite fields. Those functions can be represented as the sum of the three components: one is the trace function of the monomial term λx <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> or it multiplying by the trace function of x; the second, the sum of all the quadratic monomial functions except for one; and the third, the product of two linear functions where the parameter λ and variable x belong to an arbitrary binary finite field of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> elements for n odd.