Reviewerly/exHarmony huggingface dataset:
A novel neural network for improved in-hospital mortality prediction with irregular and incomplete multivariate data

Accurate estimation of in-hospital mortality based on patients’ physiological time series data improves the performance of the clinical decision support systems and assists hospital providers in allocating resources. In practice, the data quality issues of missing values are ubiquitous in electronic health records (EHRs). Since the vital signs are usually observed with irregular temporal intervals and different sampling rates, it is challenging to predict clinical outcomes with sparse and incomplete multivariate time series. We propose an auto-regressive recurrent neural network (RNN) based model, dubbed the bi-directional recursive encoder–decoder network (BiRED), to jointly perform data imputation and mortality prediction. To capture complex patterns of medical time sequences, a 2D cross-regression with an RNN unit (2DCR-RNN) and an imputation block with an RNN unit (IB-RNN) are designed as the recurrent component of the encoder and decoder, respectively. Furthermore, a state initialization method is proposed to alleviate errors accumulated in the generated sequence. The experimental results on two real EHR datasets show that our proposed method can predict hospital mortality with high AUC scores.
A study on the relationship between physical exercise and teenagers' happiness from the perspective of self-determination theory

In the current physical education, adolescents are required to change passive participation to active participation in the exercise process, and motivation is the key factor of individual active participation.This study adopts the motivation theory related to happiness-self-determination theory, combines the research status of happiness at home and abroad, the research status of self-determination theory, the application status and existing problems of the two in the field of sports, and puts forward the development space of this research field in the future.
Facial Recognition Technology: A Comprehensive Overview

This paper provides an extensive review of facial recognition technology, tracing its historical evolution, exploring its functioning and applications, discussing the challenges it presents, and contemplating future prospects.The technology's inception and advancement are traced from its early stages to the current state, highlighting the key developments that have shaped its progression.An exploration of various types of facial recognition systems, including 2D, 3D, and thermal, underscores the diversity and complexity of this technology.A detailed explanation of how facial recognition works is provided, outlining the processes of data acquisition, face detection, feature extraction, and matching.We further delve into the broad array of its applications across multiple domains, such as security and surveillance, smartphone authentication, social media, healthcare, and retail.Despite the impressive benefits and applications of facial recognition technology, it also presents notable challenges.These include accuracy concerns, privacy and ethical implications, and the need for comprehensive regulatory frameworks.The paper concludes with a forward-looking discussion on the future of facial recognition technology, considering potential innovations and growth predictions.This review provides a comprehensive understanding of facial recognition technology, underscoring its relevance in our digitally driven world and the implications it holds for the future.
Discussion on the Application of Quantum Teleportation Technology in the Internet of Vehicles

With the continuous progress of technologies such as lasers, electronics, and computers, their practical applications have also made great progress.Quantum Key Distribution (QKD) is currently one of the most promising quantum teleportation technologies and a hot topic in the field of secure communication.The purpose of this article was to study the application of quantum teleportation technology in the Internet of Vehicles (IoV).This article fully explained the concept, principles, and methods of quantum teleportation technology, deeply understood the absolute security attributes of quantum teleportation technology, and then pointed out its practical operability in the field of information security by introducing its development status and application prospects.Based on the security needs of the IoV and considering the shortcomings of existing security protection technologies, the risks of the IoV and the urgent need for high security of information technology were highlighted.A persistent and secure bidirectional authenticated QKD protocol was proposed, and simulation experiments were conducted on the NS-2.45 network simulation platform.The experimental results showed that the average time cost of this scheme was only 0.11 seconds when the number of vehicles was 10.Compared with other similar schemes, this scheme had lower overhead.
Better Prevention Than Cure: Cybersecurity Risk and Clawback Provision

The escalation of online transactions recently has given rise to the digitalization trend, which opens up both emerging opportunities and associated risks, across businesses. This study examines the impact of cybersecurity risk on the adoption of clawback policies among U.S. listed firms during the 2008-2018 period. Using fixed effect estimators, I reveal that clawback adoptions are more likely when cybersecurity risk is on the rise. This effect is transmitted via three channels of multiple business objectives, manager’s quiet life and market efficiency. This effect is diminished among firms with stronger commitments to technological advancement and those whose boards are occupied by co-opted directors. This study’s findings suggest that firms value the preventive property of clawback policies on corporate misconduct and that firms factor cybersecurity risk into clawback adoption decision-making.
The role of network structure in integrated water management: a case study of collaboration and influence for adopting nature-based solutions

Introduction Integrated water management (IWM) involves a range of policies, actions, and organizational processes that go beyond traditional hydrology to consider multifaceted aspects of complex water resource systems. Due to its transdisciplinary nature, IWM comprises input from diverse stakeholders, each with unique perceptions, values, and experiences. However, stakeholders from differing backgrounds may disagree on best practices and collective paths forward. As such, successful IWM must address key governance principles (e.g., information flow, collective decision-making, and power relations) across social and institutional scales. Here, we sought to demonstrate how network structure impacts shared decision-making within IWM. Methods We explored a case study in Houston, Texas, USA, where decision-making stakeholders from various sectors and levels of governance engaged in a participatory modeling workshop to improve adoption of nature-based solutions (NBS) through IWM. The stakeholders used fuzzy cognitive mapping (FCM) to define an IWM model comprising multifaceted elements and their interrelationships, which influenced the adoption of NBS in Houston. We applied grounded theory and inductive reasoning to categorize tacit belief schemas regarding how stakeholders viewed themselves within the management system. We then used FCM-based modeling to explore how unique NBS policies would translate into more (or less) NBS adoption. Finally, we calculated specific network metrics (e.g., density, hierarchy, and centrality indices) to better understand the structure of human-water relations embedded within the IWM model. We compared the tacit assumptions about stakeholder roles in IWM against the quantitative degrees of influence and collectivism embedded within the stakeholder-defined model. Results and discussion Our findings revealed a mismatch between stakeholders' external belief statements about IWM and their internal assumptions through cognitive mapping and participatory modeling. The case study network was characterized by a limited degree of internal coordination (low density index), high democratic potential (low hierarchy index), and high-efficiency management opportunities (high centrality index), which transcended across socio-institutional scales. These findings contrasted with several of the belief schemas described by stakeholders during the group workshop. We describe how ongoing partnership with the stakeholders resulted in an opportunity for adaptive learning, where the NBS planning paradigm began to shift toward trans-scale collaboration aimed at high-leverage management opportunities. We emphasize how network analytics allowed us to better understand the extent to which key governance principles drove the behavior of the IWM model, which we leveraged to form deeper stakeholder partnerships by identifying hidden opportunities for governance transformation.
Protocol for suppression of noise from stimulated multiphoton emissions in concatenated entanglement swapping links and quantum repeaters

Multiphoton emissions constitute a fundamental source of noise in quantum repeaters and other quantum communication protocols when probabilistic photon sources are employed. In this paper it is shown that by alternating the Bell state measurement basis in concatenated entanglement swapping links one can automatically identify and discard many errors from stimulated multiphoton emissions. The proposed protocol is shown to completely eliminate the dominant quadratic growth of multiphoton errors with the length of the repeater chain. Furthermore, it is shown that the protocol can be employed in satellite-assisted entanglement distribution links to enable links which are more robust in the presence of imbalanced channel losses. The analysis introduces a convenient calculus based on Clifford algebra for modeling concatenated entanglement swapping links with multiphoton emissions. In particular, we present a compact expression for the fidelity of the Bell state produced by a repeater chain of arbitrary length including noise from double-pair emissions.
The UK Reproducibility Network: A progress report

There is growing awareness that the ways in which academic research is conducted could be improved. A number of exciting innovations are emerging, alongside a broader agenda that includes a growing emphasis on open research. This short article outlines the rationale, progress and plans of the UK Reproducibility Network, which is one of a growing number of similar initiatives internationally that promote more rigorous and transparent research.
Hardware Accelerators for Digital Signature Algorithms Dilithium and FALCON

Digital signature algorithms are the foundation of many secure communication protocols, including those used in Internet of Things (IoT) applications. While the current generation of signature schemes is secure against classical attacks, they are potentially vulnerable to attacks using quantum computers. Because of this threat, multiple new schemes have been developed and evaluated in recent years. From among these schemes, the National Institute of Standards and Technology standardized two and selected additional three for near-term standardization. For use in IoT, these schemes must be sufficiently efficient in terms of their public-key and signature sizes and the timing of major operations. In this paper, we analyze the choice between two primary schemes considered for extensive use in IoT, CRYSTALS-Dilithium and FALCON, from the point of view of developing efficient hardware accelerators supporting cryptographic operations performed by IoT clients and servers.
Virtual Reality as Experiential Learning to Promote STEM-DRR in Tertiary Education

Efforts and discourses of the need to integrate disaster education into a science curriculum for university students in Indonesia are well documented. However, lecturers need more room to introduce Disaster Risk Reduction (DRR) in a way that involves students as experiential learners. Integrating DRR into Science, Technology, Engineering, and Mathematics (STEM) subjects is a crucial point. The current study used virtual reality (VR) as a pedagogical tool for learning through experience to promote STEM-DRR at tertiary levels of education. This experimental study aimed to determine the effects of VR use on students' experiential learning based on the VR online training developed activities. The methods for data collection included observation and survey. Ten participants were in the final year of their study and were recruited based on their interest in using VR media for their thesis. The results showed that half of the participants understood the training content, and 40% were classified as knowledgeable before training. In addition, significant contributions to the learning process were observed in training, where half participants were satisfied with the new skills attained, and 80% of the participants argued that the training was beneficial to their final projects. Thus, in the present study integrating STEM-DRR into the VR training content to promote students' awareness of disaster risk is considered successful. In conclusion, the results suggest that technology-enhanced learning supports distance learning and is proven economical and efficient.
Multivariate time series prediction based on quantum enhanced LSTM models

Long short-term memory (LSTM) is a widely used artificial neural network that is well suited for time series prediction. Quantum machine learning as a new research topic combines the advantages of quantum data processing and classical machine learning. In this paper, based on a hybrid quantum classical scheme, we design a quantum enhanced LSTM model and several variants such as QGRU. We also performed experiments with a multivariate time series prediction problem to verify the feasibility of these models. Through this research, we expect to explore the benefits and implementation of quantum-based machine learning.
Distributed Photovoltaic Daily Power Generation Forecast Method Based on Model Selection and Statistical Scale-up

There are certain difficulties in the data acquisition and management of distributed photovoltaic (PV) systems, resulting in a limited quantity and quality of data. Significant challenges were posed to the forecasting of distributed PV output by these issues. Given the shortage of historical data of distributed PV power stations, a distributed PV daily generation forecasting method based on model selection and statistical scale-up is proposed in this paper. Initially, the entire region is divided into smaller regions based on the geographical location information of distributed PV power stations. Subsequently, the PV power station with high-quality data in the region is selected as the reference power station, and other distributed PV power stations in the region are regarded as a virtual PV power station. The neural network model is then invoked by model selection algorithm to precisely predict the power generation of the reference power station, and the daily power generation of the virtual power station is calculated by the relationship between the power generation and installed capacity of the reference and virtual power stations. Finally, the predicted values of all regions are summed up to obtain the daily energy generation of the entire region. By taking the distributed PV power station in Huizhou, Guangdong as an example, the prediction accuracy of this area reaches 86.057%, thus verifying the effectiveness of the proposed method.
A Transferrable and Noise-Tolerant Data-Driven Method for Open-Circuit Fault Diagnosis of Multiple Inverters in a Microgrid

A new data-driven method is developed in this article for open-circuit fault diagnosis of multiple inverters in a microgrid. The diagnosis problem is hierarchically modelled as a faulty inverter localization subproblem and a switch fault classification subproblem. First, eigen-decomposition is conducted for measurement signals (bus voltage and active power) and then certain features are extracted to represent the distribution characteristics of decomposed eigenvalues. By utilizing these features as the input, extreme learning machines (ELMs) are trained to identify the faulty inverter in the microgrid based on a decision-making scheme. Given different manufacturers of the inverter, one trained switch classifier may not work for other similar inverters. Therefore, a transfer learning algorithm, domain adaption ELM, is utilized to train the switch fault classifier at the second stage. The proposed method is advantageous for much stronger robustness under noises and generalization capability compared to existing approaches, as shown in test results.
OWL-Sharp: Source Code Semantic Generator

The use of ontologies in Software Engineering has great potential for the progress of software construction processes. Ontologies can provide the inference of new knowledge and the retrieval of information in a more expressive way with the use of queries, making the evaluation of relevant knowledge effective for the context of the application. In this article, we present the OWL-Sharp tool, a semantic source code generator for solutions coded in the C Sharp programming language. We evaluated the performance of OWL-Sharp in 23 relevant opensource software solutions and performed the experiments obtaining metrics of processing of the artifacts and the execution time of each solution. To evaluate the semantic generation of the project, we performed code smell detection using semantic queries in SPARQL. The results show that the tool achieves significant results.
Deep Color Compensation for Generalized Underwater Image Enhancement

Underwater images suffer from quality degradation due to the underwater light absorption and scattering. It remains challenging to enhance underwater images using deep learning-based methods since the scarcity of real-world underwater images and their enhanced counterparts. Although existing works manually select well-enhanced images as reference images to train enhancement networks in an end-to-end manner, their performance tends to be inferior in some scenarios. We argue that the manually selected reference images cannot approximate their ground truth perfectly, leading to imbalanced learning and domain shift in enhancement networks. To address this issue, we analyse widely used underwater datasets from the perspective of color spectrum distribution and surprisingly find the sound color spectrum distribution of the enhanced reference images compared to in-air datasets. Based on this perceptive observation, instead of directly learning the enhancement mapping, we propose a novel methodology to learn color compensation for general purposes. Specifically, we present a probabilistic color compensation network that estimates the probabilistic distribution of colors by multi-scale volumetric fusion of texture and color features. We further propose a novel two-stage enhancement framework that first performs color compensation and then enhancement, which is highly flexible to be integrated with an existing enhancement method without tuning. Extensive experiments on underwater image enhancement across various challenging scenarios show that our proposed approach consistently improves the results of the popular conventional and learning-based methods by a significant margin. Moreover, our enhanced images achieve superior performance on underwater salient object detection and visual 3D reconstruction, demonstrating that our method can successfully break through the generalization bottleneck of existing learning-based enhancement models. Our implementation will be made available at https://github.com/Ray2OUC/P2CNet.
SU2GE-Net: a saliency-based approach for non-specific class foreground segmentation

Salient object detection is vital for non-specific class subject segmentation in computer vision applications. However, accurately segmenting foreground subjects with complex backgrounds and intricate boundaries remains a challenge for existing methods. To address these limitations, our study proposes SU2GE-Net, which introduces several novel improvements. We replace the traditional CNN-based backbone with the transformer-based Swin-TransformerV2, known for its effectiveness in capturing long-range dependencies and rich contextual information. To tackle under and over-attention phenomena, we introduce Gated Channel Transformation (GCT). Furthermore, we adopted an edge-based loss (Edge Loss) for network training to capture spatial-wise structural details. Additionally, we propose Training-only Augmentation Loss (TTA Loss) to enhance spatial stability using augmented data. Our method is evaluated using six common datasets, achieving an impressive [Formula: see text] score of 0.883 on DUTS-TE. Compared with other models, SU2GE-Net demonstrates excellent performance in various segmentation scenarios.
Library of the General and Main Staff and its Employees (1811—1917)

Due to the lack of a comprehensive study of the employees of the Library of the General and Main Staff, this article is the first to provide the most comprehensive list of its leaders and library personnel and to describe them by such criteria as education, military or civilian rank, military experience, scientific publications, historical research, membership in the Imperial Russian Military Historical Society. The primary objectives of this study were: to identify the names of librarians and their assistants, to chronicle their occupation of these positions and to determine the time frame of each of them in their post. The article uses chronological, source and comparative analysis methods. For the solution of the problem we assembled a large representative source base including 68 address calendars, 22 monthly calendars, 10 yearbooks, 7 lists of books received by the Library of the General and Main Staff between 1911 and 1917, as well as pre-revolutionary publications on the history of the General Staff and its book collection. For the first time the author divides into periods the history of the Library of the General and Main Staff in pre-revolutionary period of its existence — from 1811 to 1917. The most important result of the research, new for historiography, is the full reconstruction of the list of librarians and library personnel and chronicle of their posts, which are set out in accordance with the worked out periodization. Within each of the periods, the most important events in the history of the library are indicated, which enables the contribution of librarians and librarians in its functioning and development to be evaluated.
Neuro-Symbolic AI for Sensor-based Human Performance Prediction; System Architectures and Applications

Recently, due to the rapid development of deep learning methods, there has been a growing interest in Neurosymbolic Artificial Intelligence, which takes advantage of both explicit symbolic knowledge and statistical subsymbolic neural knowledge representations.In sensor-based human performance prediction (HPP) for safety-critical applications, where maintaining optimal human and system performance is a major concern, neuro-symbolic AI systems can improve sensor-based HPP tasks in complex working settings.In this paper, we focus on the advantages of hybrid neuro-symbolic AI systems, present the outstanding challenges and propose possible solutions for HPP in the safety-critical application domain.