Reviewerly/exHarmony huggingface dataset:
Remote Sensing Novel View Synthesis With Implicit Multiplane Representations

Novel view synthesis of remote sensing scenes is of great significance for scene visualization, human-computer interaction, and various downstream applications.Despite the recent advances in computer graphics and photogrammetry technology, generating novel views is still challenging particularly for remote sensing images due to its high complexity, view sparsity and limited view-perspective variations.In this paper, we propose a novel remote sensing view synthesis method by leveraging the recent advances in implicit neural representations.Considering the overhead and far depth imaging of remote sensing images, we represent the 3D space by combining implicit multiplane images (MPI) representation and deep neural networks.The 3D scene is reconstructed under a self-supervised optimization paradigm through a differentiable multiplane renderer with multi-view input constraints.Images from any novel views thus can be freely rendered on the basis of the reconstructed model.As a by-product, the depth maps corresponding to the given viewpoint can be generated along with the rendering output.We refer to our method as Implicit Multiplane Images (ImMPI).To further improve the view synthesis under sparse-view inputs, we explore the learning-based initialization of remote sensing 3D scenes and proposed a neural network based Prior extractor to accelerate the optimization process.In addition, we propose a new dataset for remote sensing novel view synthesis with multi-view real-world google earth images.Extensive experiments demonstrate the superiority of the ImMPI over previous state-of-the-art methods in terms of reconstruction accuracy, visual fidelity, and time efficiency.Ablation experiments also suggest the effectiveness of our methodology design.Our dataset and code can be found at https:
DeSci-Decentralized Science

Fundamental science and applied research and technology development (RTD) are facing significant challenges that particularly compound to the notorious credibility, reproducibility, funding and sustainability crises. The underlying, serious shortcomings are substantially amplified by a metrics-obsessed publication culture, and a growing cohort of academics fishing for fairly stagnant (public) funding budgets. This work presents, for the first time, a groundbreaking strategy to successfully address these severe issues; the novel strategy proposed here leverages the distributed ledger technology (DLT) &amp;ldquo;blockchain&amp;rdquo; to capitalize on cryptoeconomic mechanisms, such as tokenization, consensus, crowdsourcing, smart contracts, reputation systems as well as staking, reward and slashing mechanisms. This powerful toolbox, which is so far widely unfamiliar to traditional scientific and RTD communities (&amp;ldquo;TradSci&amp;rdquo;), is synergistically combined with the exponentially growing computing capabilities for virtualizing experiments through digital twin methods in a future scientific &amp;ldquo;metaverse&amp;rdquo;. Project contributions, such as hypotheses, methods, experimental data, modelling, simulation, assessment, predictions and directions are crowdsourced using blockchain, and captured by so-called non-fungible tokens (&amp;ldquo;NFTs&amp;rdquo;). The so enabled, highly integrative approach, termed decentralized science (&amp;ldquo;DeSci&amp;rdquo;), is destined to move research out of its present silos, and to markedly enhance quality, credibility, efficiency, transparency, inclusiveness, sustainability, impact, and sustainability of a wide spectrum of academic and commercial research initiatives.
Comparative study of extraction features and regression algorithms for predicting drought rates

Rice is the primary staple food source for Indonesian people, with consumption increasing so that rice production needs to be increased. Rice drought is one of the problems that can hamper rice production. This research aims to determine the best extraction feature between the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) in describing rice fields’ dryness. Moreover, using the random forest regression algorithm. This research compares NDVI with NDWI using data originating from Sentinel-2A and retrieved via the google earth engine. Regression algorithms are used in research to predict drought in paddy fields. This research shows that NDVI is better than NDWI in predicting drought using random forest regression algorithms and logistic regression algorithms. The random forest regression algorithm based on the results obtained shows that the average root mean square error (RMSE) on NDVI is 0.018, and NDWI is 0.012. Based on the logistic regression algorithm results, it was found that the average value of RMSE on NDVI was 0.346, and NDWI was 0.336. Based on the results of the RMSE, it shows that the forecasting ability of the random forest regression algorithm is better than the logistic regression.
Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations

Increasing availability of electronic health databases capturing real-world experiences with medical products has garnered much interest in their use for pharmacoepidemiologic and pharmacovigilance studies. The traditional practice of having numerous groups use single databases to accomplish similar tasks and address common questions about medical products can be made more efficient through well-coordinated multi-database studies, greatly facilitated through distributed data network (DDN) architectures. Access to larger amounts of electronic health data within DDNs has created a growing interest in using data-adaptive machine learning (ML) techniques that can automatically model complex associations in high-dimensional data with minimal human guidance. However, the siloed storage and diverse nature of the databases in DDNs create unique challenges for using ML. In this paper, we discuss opportunities, challenges, and considerations for applying ML in DDNs for pharmacoepidemiologic and pharmacovigilance studies. We first discuss major types of activities performed by DDNs and how ML may be used. Next, we discuss practical data-related factors influencing how DDNs work in practice. We then combine these discussions and jointly consider how opportunities for ML are affected by practical data-related factors for DDNs, leading to several challenges. We present different approaches for addressing these challenges and highlight efforts that real-world DDNs have taken or are currently taking to help mitigate them. Despite these challenges, the time is ripe for the emerging interest to use ML in DDNs, and the utility of these data-adaptive modeling techniques in pharmacoepidemiologic and pharmacovigilance studies will likely continue to increase in the coming years.
Software Effort Estimation Development From Neural Networks to Deep Learning Approaches

Software Engineering is a branch of computers that includes the development of structured software applications. Estimation is a significant measure of software engineering projects, and the skill to yield correct effort estimates influences vital economic processes, which include budgeting and bid tenders. But it is challenging to estimate at an initial stage of project development. Numerous conventional and machine learning-based methods are utilized for estimating effort and still, it is a challenge to achieve consistency in precise predictions. In this research exploration, various ANN-based models are compared with conventional algorithmic methods. The study also presents the comparison of results on various datasets from the artificial neural network models, deep learning models, higher-order Neural Network models, leading to the conclusion that hybrid methods yield better results. This paper also includes an analysis of primary data collected from Software Project professionals using the questionnaire method involving questions related to software cost estimation.
Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning Algorithms

Nowadays, software vulnerabilities pose a serious problem, because cyber-attackers often find ways to attack a system by exploiting software vulnerabilities. Detecting software vulnerabilities can be done using two main methods: i) signature-based detection, i.e. methods based on a list of known security vulnerabilities as a basis for contrasting and comparing; ii) behavior analysis-based detection using classification algorithms, i.e., methods based on analyzing the software code. In order to improve the ability to accurately detect software security vulnerabilities, this study proposes a new approach based on a technique of analyzing and standardizing software code and the random forest (RF) classification algorithm. The novelty and advantages of our proposed method are that to determine abnormal behavior of functions in the software, instead of trying to define behaviors of functions, this study uses the Word2vec natural language processing model to normalize and extract features of functions. Finally, to detect security vulnerabilities in the functions, this study proposes to use a popular and effective supervised machine learning algorithm.
Performance Analysis of Continuous Variable Quantum Teleportation with Noiseless Linear Amplifier in Seawater Channel

Continuous variable quantum teleportation (CVQT) is one of the technologies currently explored to implement global quantum networks. Entanglement source is an indispensable resource to realize CVQT, and its distribution process has natural symmetry. Though there are many results for CVQT over optical fiber or atmospheric channel, little attention is paid to seawater channel. In this paper, a model based on seawater chlorophyll concentration is used to study the attenuation effect of seawater on light. In our scheme, a noiseless linear amplifier is utilized for enhancing the performance of CVQT under seawater channel. Simulation results show that the proposed scheme has an improvement in terms of fidelity and maximum transmission distance compared with the original scheme.
Assessing the Potential of Implementing a Solar-Based Distributed Energy System for a University Using the Campus Bus Stops

Large educational facilities hold great potential for the implementation of solar-based distributed energy systems. The aim of this paper is to present a prototype and an assessment of a solar-based bus shelter photovoltaic system intended to be implemented at a campus scale that serves as an energy-distributed system. The National Autonomous University of Mexico (UNAM), a campus with an area of 7.3 km2 and bus stops’ roof area availability of around 1100 m2 was selected as a case study. The proposed system, apart from considering on-site generation, also considers an increase in end-use services such as the installation of television screens for information, charging docks, surveillance cameras, internet service, and lighting. For the assessment, a load facility survey and an estimation of the baseline energy use was conducted based on two demand use conditions, corresponding to 12 and 24 h for different archetypical stations. It was found that the baseline annual energy consumption for all the bus stops represents from 55–111 MWh. In this paper, an initial prototype of a solar-based bus shelter PV system is presented, and an assessment is carried out to understand its potential application at a large scale. The analysis shows that energy use in the retrofitted stations would rise to 167 MWh/year; however, apart from covering on-site demand, the system has the capacity to generate an additional 175 MWh, feeding nearby university buildings. It is calculated that the system could save around 130 t CO2e annually. The economic analysis shows that the project has a discounted payback (DPB) of almost 9 years and an internal rate of return (IRR) of 5.9%; however, in scenarios where renewable generation and carbon incentives are applied, this improves the project’s DPB to 6 years and the IRR to 13%.
A Novel Forecasting Model for Solar Power Generation by a Deep Learning Framework with Data Preprocessing and Postprocessing

Photovoltaic power has become one of the most popular energy due to environmental factors. However, solar power generation has brought many challenges for power system operations. To optimize safety and reduce costs of power system operations, an accurate and reliable solar power forecasting model is significance. This study proposes a deep learning method to improve the performance of short-term solar power forecasting, which includes data preprocessing, feature engineering, Kernel Principal Component Analysis, Gated Recurrent Unit Network training mode based on time of the day classification, and post processing with error correction. Both historical solar power, solar irradiance, and Numerical Weather Prediction (NWP) data, such as temperature, irradiance, rainfall, wind speed, air pressure, humidity, are considered as input dataset in this work. As a case study, the measured solar power data from ten solar sites in Taiwan are forecasted for the next day PV power outputs with one-hour resolution. The error index such as Normalized Root Mean Squared Error (NRMSE), Normalized Mean Absolute Percent Error (NMAPE) are chosen to evaluate the performance of forecasting models. Compared with other benchmark models including ANN, LSTM, XGBoost, and single GRU, the experimental results by the proposed forecasting model show its high performance. Furthermore, the proposed model also demonstrates the importance of data preprocessing and post processing based on error correction.
SCHOOLTEACHERS-PARENTS INTERACTIONS AMID DISTANCE AND BLENDED LEARNING: TWO-YEAR EXPERIENCE OF OVERCOMING NEGATIVE INFLUENCES OF COVID-19 PANDEMIC

The paper aims to show the shifts within the paradigm of schoolteachers-parents interactions amid distance and blended learning. The authors examine two-year experience of overcoming negative influence of the Covid-19 pandemic onto the system of general secondary school education. Two web-based questionnaires (for schoolteachers and for parents) were developed using Google Forms. The main objective is to get information from schoolteachers, on the one hand, and from parents, on the other hand, as for challenges they faced amid blended learning caused by the influence of the Covid-19 pandemic on the system of general secondary school education. The research methodology targeted at summarising theoretical issues on the topic and collecting empirical data comprises both the theoretical method of analysis and synthesis and the empirical method of web-based surveys. The research sample includes 2269 respondents (619 general secondary schoolteachers and 1650 parents) from different regions of Ukraine. As a general conclusion the researchers identified and substantiated the system of psychological and didactic tasks that are to be solved for harmonising further schoolteachers-parents interactions amid distance and blended learning caused by the Covid-19 pandemic. Moreover, the researchers do believe that the existing theoretical and methodological foundations of organising and providing the educational process at the stage of general secondary education are to be radically reviewed.
Ubiquitous Tourist System Based on Multicriteria Decision Making and Augmented Reality

Increasing global demand for travel has drawn public attention to the tourism industry. This industry needs the design of intelligent systems based on new concepts to facilitate better service delivery. To this end, this study proposes a ubiquitous tourist system based on context-awareness, multicriteria decision making (MCDM), and augmented reality (AR) using a geospatial information system (GIS). This system provides two services to the user. First, it recommends a hotel in the vicinity of the user based on their preferences. Subsequently, it modifies the information property to augment the information concerning the visited object using AR technology. This system offers the advantage of adapting its models based on the user and their environment using context-awareness, thereby facilitating increased system automation during service delivery. Furthermore, this system enables personalization based on user needs. Our system was evaluated via a usability test using a Likert scale based on two system aspects, namely, system design, and user acceptance of the result. The output of this test yielded an average score of 4.112. The proximity of this score to the highest level of the Likert scale indicates the acceptance of the system by users.
On Natural Language User Profiles for Transparent and Scrutable Recommendation

Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years. We focus on the challenge of supporting people's understanding and control of these systems and explore a fundamentally new way of thinking about representation of knowledge in recommendation and personalization systems. Specifically, we argue that it may be both desirable and possible for algorithms that use natural language representations of users' preferences to be developed. We make the case that this could provide significantly greater transparency, as well as affordances for practical actionable interrogation of, and control over, recommendations. Moreover, we argue that such an approach, if successfully applied, may enable a major step towards systems that rely less on noisy implicit observations while increasing portability of knowledge of one's interests.
DIGITAL RESPONSIBILITY INSIGHTS FROM A CROSS-CULTURAL DESIGN THINKING WORKSHOP FOR CREATIVITY

To be digitally responsible, one should be aware of the current issues in the digital world; therefore, raising awareness on digital responsibility and data privacy is important in the public and private sectors. This study aimed to raise awareness on digital responsibility and help students understand the importance of utilizing the design thinking process for generating creative ideas. The authors facilitated a design thinking workshop where 62 students from different cultural backgrounds (Korean and Swiss) and majors collaboratively explored data privacy issues and presented their novel, creative solutions. The data gathered from observations and interviews were analyzed using qualitative content analysis. These students of the digital age developed remarkable ones, such as keeping data safe from social networking services and protecting voice data from being recorded by a third party. The cross-cultural, interdisciplinary team rapidly implemented their creative and globally applicable solutions with visually feasible, diagrammed prototypes, which is an effective tool of the design thinking process. The article demonstrates how design thinking techniques could be applied in university classes and could inspire teachers to use the five stages of design thinking in their teaching.
Geospatial Artificial Intelligence for Early Detection of Forest and Land Fires

Over the years, early detection of forest and land fires has been conducted using hotspot data provided by the National Institute of Aeronautics and Space (LAPAN), based on its interpretation of satellite images. The hotspot data have tremendously helped firefighting efforts and further enforcement. However, the system has several shortcomings, especially due to its inability to distinguish forest and land fires from other hot surfaces or fires caused by common human activities. Furthermore, this method also requires labor-intensive verification, and heavily relies on human factors for advanced analysis and validation. Recently, the DG of Law Enforcement of the Ministry of Environment and Forestry (DGLE MoEF) has been piloting a new approach through advancement in artificial intelligence, called Geospatial Artificial Intelligence (GeoAI). By utilizing recorded satellite image data from 2017 - 2019, the machine has been trained to recognize the pattern and tone of the image in burnt areas so that it can validate the presence of the burnt area based on the history of Sentinel-2 imagery for the past week at each cluster. DGLE MoEF found that the burnt area data processed by GeoAI has better accuracy than the hotspot count for forest and land fire identification. Moreover, GeoAI may ease forest and land fire analysis and verification by automatically overlaying forest area and company concessions at the burnt area. GeoAI’s innovation in forest and land fire monitoring can produce more accurate and complete early detection data of forest and land fires than currently available hotspot data. The results of hotspot clustering that detect fires may assist firefighters in rapidly extinguishing the fire, and support law enforcement officers in determining the appropriate target location. Therefore, GeoAI technology may increase the effectiveness and efficiency of resources allocated by law enforcement officers in providing better and more responsive public services.&#x0D; Keywords: GeoAI, geospatial technology, artificial intelligence, forest fire, hotspot
A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital "Istituti Clinici Salvatore Maugeri" in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.
Human-like property induction is a challenge for large language models

The impressive recent performance of large language models such as GPT-3 has led many to wonder to what extent they can serve as models of general intelligence or are similar to human cognition. We address this issue by applying GPT-3 to a classic problem in human inductive reasoning known as property induction. Our results suggest that while GPT-3 can qualitatively mimic human performance for some inductive phenomena (especially those that depend primarily on similarity relationships), it reasons in a qualitatively distinct way on phenomena that require more theoretical understanding. We propose that this emerges due to the reasoning abilities of GPT-3 rather than its underlying representations, and suggest that increasing its scale is unlikely to change this pattern.