Reviewerly/exHarmony huggingface dataset:
Virtual Reality Storytelling: Pedagogy and Applications

The Spirit of Egypt is a Virtual Reality project being developed by Exelauno. This project, which recreates an ancient Egyptian funeral procession and transition into the Hereafter, is being designed with instructional pedagogy in mind and, thus, can be an example through which to discuss real-world, best practices. If employed correctly, Virtual Reality is able not only to help students learn about ancient Egypt but also make it more memorable. While there are great benefits to bringing technology into the classroom, real criticisms exist: the learning curve for “digital immigrants” can be steep; developing technology can diverge from Universal Design standards; and screens can negatively affect learning and retention for all students. Our assertion is that technology needs to be brought into the classroom in thoughtful, purposeful, and pedagogically sound ways. Uniquely, compared to other technologies that put something (i.e., a screen) between students and the information, VR in essence does the opposite—completely immersing students in the world they are studying.
COVID-19: How did the pandemic impacted the economic-financial results of the healthcare companies included in B3?

This study aims to verify the influence of the pandemic on the economic and financial results of the companies included in the health sub-sectors of B3. The sample consisted of 22 companies with data treated by test Wilcoxon and panel regression. The results indicate that the firms were able to meet their short- and long-term obligations. The medical services subsector (hospitals, analysis, and diagnostics) showing the worst results with significant increase in third-party capital and a reduction in net margin and profitability. This study expands on the scarce literature on the health sector, which is critical to the well-being and economy of Brazilians and evidence to stakeholders that economic-financial results differs between subsectors.
A Critical Review on the Use of Threshold Value in Calculating Similarity for Determining Copyright Infringement

Quantitative similarity is widely used in copyright infringement lawsuits of computer programs.There are several procedures in the process of calculating quantitative similarity.Among them, the procedure for applying the criteria for similarity calculation by setting a threshold value is important in that it directly affects the results of quantitative similarity calculation.If it is overlooked, the calculated similarity may be distorted to the extent that it is inappropriate to be used as a basis for determining the actual substantial similarity, so it should be handled carefully.Although the method of threshold value setting is widely used in practice, little discussion has been made on the threshold value.However, the procedure for using threshold value reveals several problems while quantitatively presenting standards of similarity and playing a role in limiting the subject of protection under copyright law.In this study, the problems were regarded as the essential problems of similar judgment, difficulty in distinguishing expressions, and practical limitations, and it looked at ways to improve these problems.
Diversity as a Game Changer for Successful Teams in RoboCup [Women in Engineering]

The international RoboCup competition has, in the past, been described as “a treasure trove of rich diversity for research issues and interdisciplinary connections” <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . This description is often credited to the facets of the federation itself, which hosts a variety of challenges for rescue robots, robots for service at home and in industrial environments, and, most prominently, robots that play soccer, in different sizes and formats, be they simulated, wheeled, or legged.
The Square Theory of Economic Development

Economic development is a complex process that involves multiple factors and dimensions. The square theory of economic development proposes that there are five distinct levels that must be addressed in order to foster sustainable economic growth. These levels include natural resources, infrastructure, investment climate, human capital, innovation, and entrepreneurship. This theory provides a framework for policymakers and researchers to better understand the factors that drive economic development to design effective policies and strategies to promote growth.
Students’ Perception of Using E-book to Improve Literacy Skill at the English Education Department of UNISKA Banjarmasin

This research aimed to explore the students’ perception of using e-book to improve literacy skill. It was quantitative method by using survey design. The sample in this research was the third-semester students at the English Education Department of UNISKA Banjarmasin in the academic year 2022/2023. In collecting the data, the close ended questionnaire was used to explore the students’ perceptions of using e-book to improve literacy skill. There were 20 statements which divided into four indicators, namely: 1) motivation and interest, 2) effectiveness, 3) flexibility, and 4) preferences. It was found that most of the students attained positive perception on the motivation and interest, effectiveness, and preferences. Then, they had very positive perception on the flexibility. Moreover, the overall result of the students’ perception showed that the highest percentage was agreed with 54% and the mean score was 3.98. It can be concluded that most of the students on the third-semester at the English Education Department of UNISKA Banjarmasin in the academic year 2022/2023 agreed and they attained positive perception of using e-book to improve literacy skill.
Employee Attendance Application Using QR Code Android-Based at Eria Hospital Pekanbaru

Eria Hospital has implemented a fingerprint attendance system, but it has some limitations such as difficulty in reading fingerprints, limited access to fingerprint scanner which is only located in one place, and dependence on shared equipment. To overcome these problems, an Android-based attendance application was developed using Waterfall software design. This application uses QR code and GPS to make it easier for employees to record their attendance while in the work area. The quality of this application is based on ISO 25010 quality standards, which include functional testing with all functionalities working well, reliability testing with an A rating, compatibility testing with split-screen feature compatibility, security testing with an A rating, portability testing with results can be install on different devices and Android versions 8.1 to 12, maintainability testing with an A rating, efficiency testing with a startup time of around 1.6 seconds, and user experience testing with attractiveness score of 2.62, perspicuity score of 2.57, efficiency score of 2.62, dependability score of 2.51, stimulation score of 2.62, and novelty score of 2.40. Overall, the implementation of an Android-based attendance application using QR Code has proved to be a viable solution to the challenges faced by the fingerprint attendance system, and has contributed to the optimization of attendance data collection and processing in Eria Hospital.
Review on Wearable Sensors of the Medical System

A framework known as "smart healthcare" makes use of technologies like wearable's, and the Internet of Medical Things (IoT), In order to intelligently manage and respond to the needs of the health environment, we need to combine data, machine learning algorithms, wireless communication technology, and the Internet of Things (IoT) to link people, resources, and organizations; make health records easily accessible; and share the data widely. Medical sensors, or IoT, are one of the key components of smart healthcare. Due to the complexity of illnesses, Disease diagnosis often necessitates the use of multiple types of medical signals. The most crucial concern when employing multimodal signals is how to fuse them, which is a topic of growing interest among researchers. The newest generation of personal portable gadgets for telemedicine practice is wearable health monitoring systems. However, due to the inapplicability of the building process used for standard semiconductor equipment, wearable healthcare equipment research and commercialization are currently progressing at a very sluggish rate. Despite these obstacles, developments in materials flag, chemical analysis techniques, apparatus, and manufacturing processes created a groundwork for whole new wearable technology, which has continued to evolve. &nbsp
ClinicLens: Visual Analytics for Exploring and Optimizing the Testing Capacity of Clinics given Uncertainty

Clinic testing plays a critical role in containing infectious diseases such as COVID-19. However, one of the key research questions in fighting such pandemics is how to optimize testing capacities across clinics. In particular, domain experts expect to know exactly how to adjust the features that may affect testing capacities, given that dynamics and uncertainty make this a highly challenging problem. Hence, as a tool to support both policymakers and clinicians, we collaborated with domain experts to build ClinicLens, an interactive visual analytics system for exploring and optimizing the testing capacities of clinics. ClinicLens houses a range of features based on an aggregated set of COVID-19 data. It comprises Back-end Engine and Front-end Visualization that take users through an iterative exploration chain of extracting, training, and predicting testing-sensitive features and visual representations. It also combines AI4VIS and visual analytics to demonstrate how a clinic might optimize its testing capacity given the impacts of a range of features. Three qualitative case studies along with feedback from subject-matter experts validate that ClinicLens is both a useful and effective tool for exploring the trends in COVID-19 and optimizing clinic testing capacities across regions. The entire approach has been open-sourced online: https://github.com/YuDong5018/clinic-lens.
SELF-VS: Self-supervised Encoding Learning For Video Summarization

Despite its wide range of applications, video summarization is still held back by the scarcity of extensive datasets, largely due to the labor-intensive and costly nature of frame-level annotations. As a result, existing video summarization methods are prone to overfitting. To mitigate this challenge, we propose a novel self-supervised video representation learning method using knowledge distillation to pre-train a transformer encoder. Our method matches its semantic video representation, which is constructed with respect to frame importance scores, to a representation derived from a CNN trained on video classification. Empirical evaluations on correlation-based metrics, such as Kendall's $\tau$ and Spearman's $\rho$ demonstrate the superiority of our approach compared to existing state-of-the-art methods in assigning relative scores to the input frames.
Adaptive Voronoi NeRFs

Neural Radiance Fields (NeRFs) learn to represent a 3D scene from just a set of registered images. Increasing sizes of a scene demands more complex functions, typically represented by neural networks, to capture all details. Training and inference then involves querying the neural network millions of times per image, which becomes impractically slow. Since such complex functions can be replaced by multiple simpler functions to improve speed, we show that a hierarchy of Voronoi diagrams is a suitable choice to partition the scene. By equipping each Voronoi cell with its own NeRF, our approach is able to quickly learn a scene representation. We propose an intuitive partitioning of the space that increases quality gains during training by distributing information evenly among the networks and avoids artifacts through a top-down adaptive refinement. Our framework is agnostic to the underlying NeRF method and easy to implement, which allows it to be applied to various NeRF variants for improved learning and rendering speeds.
Amortized Analysis via Coinduction

Amortized analysis is a program cost analysis technique for data structures in which the cost of operations is specified in aggregate, under the assumption of continued sequential use. Typically, amortized analyses are presented inductively, in terms of finite sequences of operations. We give an alternative coinductive formulation and prove that it is equivalent to the standard inductive definition. We describe a classic amortized data structure, the batched queue, and outline a coinductive proof of its amortized efficiency in $\textbf{calf}$, a dependent type theory for cost analysis.
A comment to "A General Theory of IR Evaluation Measures"

The paper "A General Theory of IR Evaluation Measures" develops a formal framework to determine whether IR evaluation measures are interval scales. This comment shows some limitations about its conclusions.
KeyMatchNet: Zero-Shot Pose Estimation in 3D Point Clouds by Generalized Keypoint Matching

In this paper, we present KeyMatchNet, a novel network for zero-shot pose estimation in 3D point clouds. The network is trained to match object keypoints with scene-points, and these matches are then used for pose estimation. The method generalizes to new objects by using not only the scene point cloud as input but also the object point cloud. This is in contrast with conventional methods where object features are stored in network weights. By having a generalized network we avoid the need for training new models for novel objects, thus significantly decreasing the computational requirements of the method. However, as a result of the complexity, zero-shot pose estimation methods generally have lower performance than networks trained for a single object. To address this, we reduce the complexity of the task by including the scenario information during training. This is generally not feasible as collecting real data for new tasks increases the cost drastically. But, in the zero-shot pose estimation task, no retraining is needed for new objects. The expensive data collection can thus be performed once, and the scenario information is retained in the network weights. The network is trained on 1,500 objects and is tested on unseen objects. We demonstrate that the trained network can accurately estimate poses for novel objects and demonstrate the ability of the network to perform outside of the trained class. We believe that the presented method is valuable for many real-world scenarios. Code, trained network, and dataset will be made available at publication.
Carolina: a General Corpus of Contemporary Brazilian Portuguese with Provenance, Typology and Versioning Information

This paper presents the first publicly available version of the Carolina Corpus and discusses its future directions. Carolina is a large open corpus of Brazilian Portuguese texts under construction using web-as-corpus methodology enhanced with provenance, typology, versioning, and text integrality. The corpus aims at being used both as a reliable source for research in Linguistics and as an important resource for Computer Science research on language models, contributing towards removing Portuguese from the set of low-resource languages. Here we present the construction of the corpus methodology, comparing it with other existing methodologies, as well as the corpus current state: Carolina's first public version has $653,322,577$ tokens, distributed over $7$ broad types. Each text is annotated with several different metadata categories in its header, which we developed using TEI annotation standards. We also present ongoing derivative works and invite NLP researchers to contribute with their own.
LinK: Linear Kernel for LiDAR-based 3D Perception

Extending the success of 2D Large Kernel to 3D perception is challenging due to: 1. the cubically-increasing overhead in processing 3D data; 2. the optimization difficulties from data scarcity and sparsity. Previous work has taken the first step to scale up the kernel size from 3x3x3 to 7x7x7 by introducing block-shared weights. However, to reduce the feature variations within a block, it only employs modest block size and fails to achieve larger kernels like the 21x21x21. To address this issue, we propose a new method, called LinK, to achieve a wider-range perception receptive field in a convolution-like manner with two core designs. The first is to replace the static kernel matrix with a linear kernel generator, which adaptively provides weights only for non-empty voxels. The second is to reuse the pre-computed aggregation results in the overlapped blocks to reduce computation complexity. The proposed method successfully enables each voxel to perceive context within a range of 21x21x21. Extensive experiments on two basic perception tasks, 3D object detection and 3D semantic segmentation, demonstrate the effectiveness of our method. Notably, we rank 1st on the public leaderboard of the 3D detection benchmark of nuScenes (LiDAR track), by simply incorporating a LinK-based backbone into the basic detector, CenterPoint. We also boost the strong segmentation baseline's mIoU with 2.7% in the SemanticKITTI test set. Code is available at https://github.com/MCG-NJU/LinK.
Bayesian Computation in Astronomy: Novel methods for parallel and gradient-free inference

The goal of this thesis is twofold; introduce the fundamentals of Bayesian inference and computation focusing on astronomical and cosmological applications, and present recent advances in probabilistic computational methods developed by the author that aim to facilitate Bayesian data analysis for the next generation of astronomical observations and theoretical models. The first part of this thesis familiarises the reader with the notion of probability and its relevance for science through the prism of Bayesian reasoning, by introducing the key constituents of the theory and discussing its best practices. The second part includes a pedagogical introduction to the principles of Bayesian computation motivated by the geometric characteristics of probability distributions and followed by a detailed exposition of various methods including Markov chain Monte Carlo (MCMC), Sequential Monte Carlo (SMC), and Nested Sampling (NS). Finally, the third part presents two novel computational methods (Ensemble Slice Sampling and Preconditioned Monte Carlo) and their respective software implementations (zeus and pocoMC). [abridged]
DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic reflectance. To exploit cues from shadow and reflectance to solve UPS and improve performance on general materials, we propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Unlike most previous methods that use non-differentiable shadow maps and assume isotropic material, our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths. Experiments on multiple real-world datasets demonstrate our superior and robust performance.
Research on Efficiency Analysis of Microservices

With the maturity of web services, containers, and cloud computing technologies, large services in traditional systems (e.g. the computation services of machine learning and artificial intelligence) are gradually being broken down into many microservices to increase service reusability and flexibility. Therefore, this study proposes an efficiency analysis framework based on queuing models to analyze the efficiency difference of breaking down traditional large services into n microservices. For generalization, this study considers different service time distributions (e.g. exponential distribution of service time and fixed service time) and explores the system efficiency in the worst-case and best-case scenarios through queuing models (i.e. M/M/1 queuing model and M/D/1 queuing model). In each experiment, it was shown that the total time required for the original large service was higher than that required for breaking it down into multiple microservices, so breaking it down into multiple microservices can improve system efficiency. It can also be observed that in the best-case scenario, the improvement effect becomes more significant with an increase in arrival rate. However, in the worst-case scenario, only slight improvement was achieved. This study found that breaking down into multiple microservices can effectively improve system efficiency and proved that when the computation time of the large service is evenly distributed among multiple microservices, the best improvement effect can be achieved. Therefore, this study's findings can serve as a reference guide for future development of microservice architecture.
Few-Shot Domain Adaptation for Low Light RAW Image Enhancement

Enhancing practical low light raw images is a difficult task due to severe noise and color distortions from short exposure time and limited illumination. Despite the success of existing Convolutional Neural Network (CNN) based methods, their performance is not adaptable to different camera domains. In addition, such methods also require large datasets with short-exposure and corresponding long-exposure ground truth raw images for each camera domain, which is tedious to compile. To address this issue, we present a novel few-shot domain adaptation method to utilize the existing source camera labeled data with few labeled samples from the target camera to improve the target domain's enhancement quality in extreme low-light imaging. Our experiments show that only ten or fewer labeled samples from the target camera domain are sufficient to achieve similar or better enhancement performance than training a model with a large labeled target camera dataset. To support research in this direction, we also present a new low-light raw image dataset captured with a Nikon camera, comprising short-exposure and their corresponding long-exposure ground truth images.