Reviewerly/exHarmony huggingface dataset:
Privacy-preserving analysis of time-to-event data under nested case-control sampling

Analyses of distributed data networks of rare diseases are constrained by legitimate privacy and ethical concerns. Analytical centers (e.g. research institutions) are thus confronted with the challenging task of obtaining data from recruiting sites that are often unable or unwilling to share personal records of participants. For time-to-event data, recently popularized disclosure techniques with privacy guarantees (e.g., Differentially Private Generative Adversarial Networks) are generally computationally expensive or inaccessible to applied researchers. To perform the widely used Cox proportional hazards regression, we propose an easy-to-implement privacy-preserving data analysis technique by pooling (i.e. aggregating) individual records of covariates at recruiting sites under the nested case-control sampling framework before sharing the pooled nested case-control subcohort. We show that the pooled hazard ratio estimators, under the pooled nested case-control subsamples from the contributing sites, are maximum likelihood estimators and provide consistent estimates of the individual level full cohort HRs. Furthermore, a sampling technique for generating pseudo-event times for individual subjects that constitute the pooled nested case-control subsamples is proposed. Our method is demonstrated using extensive simulations and analysis of the National Lung Screening Trial data. The utility of our proposed approach is compared to the gold standard (full cohort) and synthetic data generated using classification and regression trees. The proposed pooling technique performs to near-optimal levels comparable to full cohort analysis or synthetic data; the efficiency improves in rare event settings when more controls are matched on during nested case-control subcohort sampling.
Time-lapse seismic at 29,000’: What can we learn?

With the advent of dedicated OBN we routinely achieve excellent repeatability and low 4D noise levels in the Deepwater GoM. Clear 4D signals for reservoirs at depths down to 25000' are regularly observed. For the field we select here, which is part-subsalt, we observe clear 4D signals at 23000' depth, interpreted as water displacing oil/gas condensate. Perhaps a little surprising, we also observe two different kinds of weak yet consistent 4D signals down to 29000' depth in a deeper reservoir developed by a single well. The fluid in this reservoir is characterized as near-critical retrograde gas condensate with a strong compositional gradient. The downdip hardening signal is interpreted as aquifer rise. Interestingly, there is a weak updip hardening signal too in a band above the well penetration. We hypothesise that this 4D effect may reflect a phase change in the fluid.
Performance Management of Official Development Assistance (ODA): Examining Qualitative Performance

In many OECD member countries, including South Korea, Official Development Assistance (ODA) has been steadily increasing. However, criticisms have been raised regarding the limited tangible impact compared to the amount of assistance provided. Hence, there is a growing emphasis on managing ODA outcomes based on the policy validity and sustainability of aid projects and on the importance of project evaluations from the recipient country's perspective. In this study, drawing on prior research, we conceptualized aid quality through sub-factors such as service quality, delivery quality, expectation levels, and social responsibility. We analyzed their effects on aid satisfaction using a structural model. Notably, we incorporated experience in the relevant field and country-specific collaboration experiences as moderating variables to validate the maturity of the aid system. Our findings suggest that social responsibility and expectation levels positively influence aid satisfaction. Conversely, experience in the relevant field and collaboration experience in specific countries showed significant impact only in certain paths. These results indicate the necessity of meeting expectation levels and fulfilling social responsibilities to enhance aid satisfaction. Furthermore, there's a demand for a sustainable management system that elucidates the causal relationships between aid environment and influencing factors and evaluates outcomes from the beneficiary country's viewpoint.
AN EVALUATION OF STEREO AND MULTIVIEW ALGORITHMS FOR 3D RECONSTRUCTION WITH SYNTHETIC DATA

Abstract. The reconstruction of 3D scenes from images has usually been addressed with two different strategies, namely stereo and multiview. The former requires rectified images and generates a disparity map, while the latter relies on the camera parameters and directly retrieves a depth map. For both cases, deep learning architectures have shown an outstanding performance. However, due to the differences between input and output data, the two strategies are difficult to compare on a common scene. Moreover, for remote sensing applications multi-view data is hard to acquire and the ground truth is either sparse or affected by outliers. Hence, in this article we evaluate the performance of stereo and multi-view architectures trained on synthetic data resembling remote sensing images. The data has been and processed and organized to be compatible with both kind of neural networks. For a fair comparison, training and testing are done only with two views. We focus on the accuracy of the reconstruction, as well as the impact of the depth range and the baseline of the stereo array. Results are presented for deep learning architectures and non-learning algorithms.
Settling Scores: Best Practice for the Maintenance of Sheet Music

While music libraries fundamentally face the same preservation concerns as any other library, their collections face unique problems. Sheet music is heavily used in distinct ways, and comes in a wide variety of sizes, shapes, and formats, all of which lead to maintenance issues that apply specifically to these materials. Arguing that genre and use inform the formats and damage we see in sheet music, this paper systematically explores the different features of scores, including bindings, sizes, and uses. I discuss the major preservation issues faced by circulating, printed music collections and suggest possible solutions.
Systematic Analysis of Quantum Key Distribution Network Simulations Tools and Platforms

Quantum Key Distribution (QKD) is one of the applications of quantum communications that is used to generate and distribute secret keys between two parties. These secrete keys are then used to establish secure communication. The security of QKD relies upon the principles of quantum mechanics, such as no-cloning theorem, that allow detection of any disturbances that quantum states could experience during transit. To facilitate these kinds of applications requires the development of quantum networks that consist of specialized hardware that is more complex than its classical counterpart. Hardware used in quantum networks is expensive, therefore it is very important to be confident in the design of network before physical implementation. This is why the development of quantum network simulation tools is of utmost importance. This research paper gives an overview and analysis of quantum network simulation tools and platforms that are capable of simulating quantum key distribution. Analysis will result in characteristics and capabilities of simulation tools and platforms that the researchers can use to choose the simulation tools most suitable for their scenario.
USING CLIENT-CENTERED APPROACH MODELS IN STUDENT PROJECTS FOR CREATION OF INFORMATION PRODUCTS

The article is devoted to studying the issue of customer focus when creating an information product within the framework of student scientific and engineering developments at a technical university (MSTU named after N. E. Bauman) and includes consideration of various student projects from the point of view of customer focus. The purpose of the study is to identify and classify student projects for creating information products or services to understand the possibilities for further commercialization of their projects. The research method was an analysis of the results of a survey of participants in the student scientific and engineering exhibition “Polytechnic” (hereinafter referred to as the Exhibition) of different years, who had a large information component in their implemented project. The developed classification forms the basis for studying the growth opportunities of a future company and the commercialization of the resulting solutions.
Computation harvesting from nature dynamics for predicting wind speed and direction

Natural phenomena generate complex dynamics because of nonlinear interactions among their components. The dynamics can be exploited as a kind of computational resource. For example, in the framework of natural computation, various natural phenomena such as quantum mechanics and cellular dynamics are used to realize general purpose calculations or logical operations. In recent years, simple collection of such nature dynamics has become possible in a sensor-rich society. For example, images of plant movement that have been captured indirectly by a surveillance camera can be regarded as sensor outputs reflecting the state of the wind striking the plant. Herein, based on ideas of physical reservoir computing, we present a methodology for wind speed and direction estimation from naturally occurring sensors in movies. Then we demonstrate its effectiveness through experimentation. Specifically using the proposed methodology, we investigate the computational capability of the nature dynamics, revealing its high robustness and generalization performance for computation.
A Study on the Educational Significance of Tourism Travel Through Yeolha-ilgi(熱河日記)

The purpose of this study is to re-examine the meaning of tourism travel in terms of an educational perspective, focusing on Park Ji-won's Yeolha ilgi. Tourism travel is the process of giving meaning to and documenting everything experienced in an unfamiliar place, away from everyday life.&#x0D; It can be reinterpreted as a purposeful journey that presupposes application in life. In addition, it raises the problem of various cruises and recreation in life, expanding knowledge at the personal and social level, and challenges and reflections in life. Therefore, tourism travel in a broad sense is in contact with the nature of life, so the educational perspective is deeply projected and needs to be reinterpreted realistically accordingly.&#x0D; As a result of reviewing Yeolha ilgi, Park Ji-won's experiential tourism trip has a multidimensional educational meaning that gives an opportunity to awaken life, motivates to consider and reflect on life, and practices and applies communication and convergence between cultures to the real world. In short, tourism travel is a journey of human life in a broad sense, and it is judged to have educational significance in that learning is constructed through the process and the convergence of each stage of the journey.
Human-Centered Evaluation of Anomalous Events Detection in Crowded Environments

Anomaly detection in crowd analysis refers to the ability to detect events and people's behaviours that deviate from normality. Anomaly detection techniques are developed to support human operators in various monitoring and investigation activities. So far, the anomaly detectors' performance evaluation derives from the rate of correctly classified individual frames, according to the labels given by the annotator. This evaluation does not make the system's performance appreciable, especially from a human operator viewpoint. In this paper, we propose a novel evaluation approach called “Trigger-Level evaluation” that is shown to be human-centered and closer to the user's perception of the system's performance. In particular, we define two new performance metrics to aid the evaluation of the usability of anomaly detectors in real-time.
PaniniQA: Enhancing Patient Education Through Interactive Question Answering

Abstract A patient portal allows discharged patients to access their personalized discharge instructions in electronic health records (EHRs). However, many patients have difficulty understanding or memorizing their discharge instructions (Zhao et al., 2017). In this paper, we present PaniniQA, a patient-centric interactive question answering system designed to help patients understand their discharge instructions. PaniniQA first identifies important clinical content from patients’ discharge instructions and then formulates patient-specific educational questions. In addition, PaniniQA is also equipped with answer verification functionality to provide timely feedback to correct patients’ misunderstandings. Our comprehensive automatic &amp; human evaluation results demonstrate our PaniniQA is capable of improving patients’ mastery of their medical instructions through effective interactions.1
Comparing Software Supply Chain Protection Approaches

Software supply chain attacks are not entirely new. Still, in recent years, a series of relevant high-impact incidents demonstrated they evolved in complexity and have brought them to scrutiny and at-tention. In response, several initiatives concerning pro-tecting against the associated risks formulated frame-works and best practice guides. This study compares some of these initiatives, analyzing taxonomies, threats, and security controls. As a result, knowledge about these initiatives was organized, verifying their simi-larities, and discussing security controls they propose to categorize and understand them. This paper contributes to understanding the subject, suggests ad-vances and future works, and properly identifies controls to prevent incidents.
Makespan-Minimizing Heterogeneous Task Allocation under Temporal Constraints

Task allocation is an essential element for determining the capability of multi-UAV systems to perform various tasks. This paper presents a procedure called a “rebalancing algorithm” for generating task-performing routes in heterogeneous multi-UAV systems. The algorithm adopts a greedy-based heuristic approach to find solutions efficiently in dynamically changing environments. A novel variable named “loitering” is introduced to satisfy temporal constraints, resulting in improved performance compared to heuristic algorithms: a sequential greedy algorithm, a genetic algorithm, and simulated annealing. The rebalancing algorithm is divided into two phases to minimize the makespan, i.e., the initial allocation and reallocation phases. Simulation results demonstrate the proposed algorithm’s effectiveness in highly constrained conditions and its suitability for heterogeneous systems. Additionally, the results show a reduction in calculation time and improved performance compared to the heuristic algorithms.
End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit diminished precision when tasked with detecting salient objects within intricate and multifaceted environments. In light of this pressing concern, this article presents an end-to-end deep neural network that aims to detect salient objects within complex environments. The study introduces an end-to-end deep neural network that aims to detect salient objects within complex environments. Comprising two interrelated components, namely a pixel-level multiscale full convolutional network and a deep encoder-decoder network, the proposed network integrates contextual semantics to produce visual contrast across multiscale feature maps while employing deep and shallow image features to improve the accuracy of object boundary identification. The integration of a fully connected conditional random field (CRF) model further enhances the spatial coherence and contour delineation of salient maps. The proposed algorithm is extensively evaluated against 10 contemporary algorithms on the SOD and ECSSD databases. The evaluation results demonstrate that the proposed algorithm outperforms other approaches in terms of precision and accuracy, thereby establishing its efficacy in salient object detection within complex environments.
Pengembangan media pembelajaran interaktif ekonomi berbasis telegram bot

The aim of the research is to make application in the formstandar teaching media products that are feasible to operate in the field of Economics subject matter of fiscal and monetary policy in Class XI in MAN 3 Malang-Donomulyo. This study includes Research of Development (R&amp;D) with ADDIE model. The stages of this model consists of 5 phases, namely 1) Analyze (Analysis); 2) Design (Design); 3) Develop (Development); 4) Implement (implementation),; and 5) Evaluate (evaluation. The increase in innovation in this media was examined in detail by media,material, and laguage experts, and direct in-class test werecarried out involving 25 students. The procedure for obtaining data was in the form of qualitative and quantitative.The data procedure was collected through observational studies, question and answer, and question sheets. The result and discussin studies indicate that innovative teaching media with telegram botas as the basis for Economic studies in Class XI IPS MAN 3 Malang-Donomulyo is recommended, because it is shown that the acquisition of media espert checks is in the ery decent category with a percentage of 92,5%. Validation of media experts 92,5% categorized as very feasible, while media esperts obtained a very feasible percetage of 86% so as very as feasible.
A study of deep active learning methods to reduce labelling efforts in biomedical relation extraction

Automatic biomedical relation extraction (bioRE) is an essential task in biomedical research in order to generate high-quality labelled data that can be used for the development of innovative predictive methods. However, building such fully labelled, high quality bioRE data sets of adequate size for the training of state-of-the-art relation extraction models is hindered by an annotation bottleneck due to limitations on time and expertise of researchers and curators. We show here how Active Learning (AL) plays an important role in resolving this issue and positively improve bioRE tasks, effectively overcoming the labelling limits inherent to a data set. Six different AL strategies are benchmarked on seven bioRE data sets, using PubMedBERT as the base model, evaluating their area under the learning curve (AULC) as well as intermediate results measurements. The results demonstrate that uncertainty-based strategies, such as Least-Confident or Margin Sampling, are statistically performing better in terms of F1-score, accuracy and precision, than other types of AL strategies. However, in terms of recall, a diversity-based strategy, called Core-set, outperforms all strategies. AL strategies are shown to reduce the annotation need (in order to reach a performance at par with training on all data), from 6% to 38%, depending on the data set; with Margin Sampling and Least-Confident Sampling strategies moreover obtaining the best AULCs compared to the Random Sampling baseline. We show through the experiments the importance of using AL methods to reduce the amount of labelling needed to construct high-quality data sets leading to optimal performance of deep learning models. The code and data sets to reproduce all the results presented in the article are available at https://github.com/oligogenic/Deep_active_learning_bioRE .
Motion planning and control of an autonomous mobile robot

The application of autonomous mobile robots (AMRs) has gradually become crucial in smart factories due to the advantages of improving production efficiency and reducing labour costs. Motion planning has been a key part of AMR control development. This paper presents motion planning and position tracking control systems of an omnidirectional wheel AMR powered by a hybrid fuel cell and battery power source. First, the kinematical and dynamic models of the AMR are introduced. The navigation system comprises three loops, with the first loop being motor control, the second loop being position tracking control, and a motion planning layer. The position data of the AMR for feedback control is obtained through sensor fusion of data from the inertial measurement unit (IMU) sensor, encoder sensor, and ranging sensor with simultaneous localisation and mapping (SLAM) algorithm. The motion planning is then applied to obtain an optimal path with the shortest distance and collision-free movement. In addition, the tracking algorithm is designed to drive the AMR to follow the optimal path and achieve high accuracy. The experimental results show a 30% improvement in tracking accuracy compared to traditional approaches and 8 hours of continuous working, which is promising for industrial applications, and the results are satisfactory in terms of both accuracy and efficiency requirements.
Qing Writers Whom Joseon Envoys Met in the Year of Daoguang and Xiangfeng -Focusing on the Data from ‘A Collection of Poetry by the Joseon People(韓客詩存)’-

Dao Guang(道光, 1821～1850) and Xian Feng(咸豐, 1851～1861) Period of the Qing Dynasty was a time when they faced internal and external conditions. During this period, evidential learning still held a leading position in the Qing Dynasty, but some writers began with a sense of crisis and promoted a useful learning for running a country, which drew support from the court and a favorable response from literature. On the other hand, Song School of Poetry had the greatest influence in literature at the time, and Song School of Poetry’s influence grew even greater with the addition of Tongchengpai writers with similar poetic tendencies.&#x0D; In this paper, the connections, exchanges, and tendencies of Qing writers who directly interacted with Joseon envoys during this period were examined based on changes in the Qing Dynasty’s academic style and literature. It is noteworthy that many of the writers who exchanged with Joseon envoys during this period were from Jinsa, so they had a high level of literature. In addition, it is presumed that many writers had a Suffering consciousness of friendship as they directly or indirectly participated in the Opium War or Taiping Heavenly Kingdom War.&#x0D; In terms of literary trends, it can be divided into Song School of Poetry and Tongchengpai , and in this paper, Zhang mu (1805-1849), who was a historian and had a very close relationship with the key figures of Song Si-pa, examined his academic trends, characteristics of poetry, and exchanges with Koreans. As for the Tongchengpai faction, the characteristics of poetry creation and friendship with Joseon writers were examined, focusing on the Zhu qi (朱琦, 1801～1861). Their academic trends and poetry style are thought to have had a certain influence on Joseon’s writers.
Dialog‐based multi‐item recommendation using automatic evaluation

Abstract In this paper, we describe a neural network‐based application that recommends multiple items using dialog context input and simultaneously outputs a response sentence. Further, we describe a multi‐item recommendation by specifying it as a set of clothing recommendations. For this, a multimodal fusion approach that can process both cloth‐related text and images is required. We also examine achieving the requirements of downstream models using a pretrained language model. Moreover, we propose a gate‐based multimodal fusion and multiprompt learning based on a pretrained language model. Specifically, we propose an automatic evaluation technique to solve the one‐to‐many mapping problem of multi‐item recommendations. A fashion‐domain multimodal dataset based on Koreans is constructed and tested. Various experimental environment settings are verified using an automatic evaluation method. The results show that our proposed method can be used to obtain confidence scores for multi‐item recommendation results, which is different from traditional accuracy evaluation.