Reviewerly/exHarmony huggingface dataset:
SIAEF/PoE: Accountability of Earnestness for encoding subjective information in Blockchain

Blockchain technology has the potential to be applied widely in supply chain operations. One such area is proactive supply chain risk management (SCRM). In this area, existing researchers have highlighted the fraudulent behaviour of supply chain partners who do not disclose information on the risks that impact their operations. Blockchain can address this problem by encoding each partner’s commitment to SCRM and achieve consensus. However, before this can be achieved, a key challenge to address is the inability of existing consensus mechanisms such as Proof of Work (PoW), Proof of Authority (PoA) and Proof of Stake (PoS) to deal with information that does not have a digital footprint. In this paper, we address this gap by proposing the Proof by Earnestness (PoE) consensus mechanism which accounts for the authenticity, legitimacy and trustworthiness of information that does not have a digital footprint. We also propose the Subjective Information Authenticity Earnestness Framework (SIAEF) as the overarching framework that assists PoE in achieving its aim. We test the applicability of SIAEF and PoE in a real-world blockchain environment by deploying it as a decentralized application (Dapp) and applying it in BscScan Testnet which is an official test blockchain network.
Rural regional economic data management based on blockchain technology

Abstract In the process of continuous development of rural economy, big data management plays an important role. Rural economic data mainly include rural labor force, added value of agriculture, forestry, fishery and animal husbandry, agricultural product output and per capita net income. Using big data to manage economic data can not only integrate and analyze a large number of data, build an economic management analysis database, but also predict and warn related risks in time and provide reasonable and effective economic management suggestions, thus ensuring the smooth operation of the economy. However, there are some shortcomings in data reliability, data quality and data management efficiency in rural regional economic data management, which have a great impact on the long-term stable development of the economy. This paper makes a systematic analysis of rural regional economic data, uses the blockchain consensus mechanism to quickly manage and analyze different types of economy in rural areas, and improves the speed of economic data analysis through smart contracts. This paper makes a comparative analysis of rural regional economic data management before and after adopting blockchain technology. In view of this phenomenon, according to the development demand of data management, this paper uses blockchain technology to effectively analyze and study the big data management of rural regional economy, and makes a comparative analysis through the tests of data operation efficiency, data quality, data management security and sharing degree. The experimental data show that the efficiency of data operation is as high as 1100 times per minute after the combination of blockchain technology, and the minimum manual modification ratio of node data is 0.24. This shows that the data quality of technical means is guaranteed. The data leakage rate per hour is at least 66%, and the data sharing volume is maintained at about 8000 views/hour and 7000 references/hour. The improvement of quality and efficiency can ensure the effectiveness of data management and improve the speed of economic development in rural areas, thus promoting good economic development. This paper uses blockchain technology to manage rural economic data, which has certain reference value for rural economic development.
Two ways to agree in D[N1&amp;N2] constructions – Romance left-conjunct agreement and Germanic morphologically resolved agreement

Hitherto unnoticed, Romance and Germanic differ with respect to the observable agreement patterns in D[N1&amp;amp;N2] constructions (e.g., this philosopher and linguist): While Romance (Italian, French, Spanish and Portuguese) exhibits left conjunct agreement, Germanic (German, Dutch and English) exhibits (morphologically) resolved agreement. In this article, I present an analysis that will derive this difference from one parametrized property: Germanic Num° has both gender and number, while Romance Num° lacks gender. The analysis will be couched in a multiple agree framework, where triggering is reduced to interface conditions: The outcome of (multiple) agree must be semantically and phonologically interpretable.
Instructional Design, Learning Satisfaction, and Learning Outcome in a Virtual Reality Learning Environment Aimed at Improving the Cognition of Computer Hardware Components

The integration of teaching materials with virtual reality (VR) technology is a common method for improving student interaction in courses, providing students with experience related to real-life spatial environments in class. This study developed a cost-effective and portable device that offers an immersive VR experience for learning the identification of computer hardware components. This device enables teachers to train technicians in computer hardware fabrication. The experimental group comprised 12 participants. According to the results of the learning satisfaction analysis, the students highly enjoyed the immersive learning experience. Descriptive statistics and the Wilcoxon matched-pairs signed-rank test are used for statistical analysis. Analysis of cognitive learning outcomes indicated that all students accurately identified all computer components after the intervention. By using the immersion teaching method, teachers could considerably improve the learning outcomes of students related to their cognition of computer hardware components.
Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability

Image animation aims to transfer the posture change of a driving video to the static object of the source image, and has potential applications in various domains, such as film and game industries. The essential part in this task is to generate a video by learning the motion from the driving video while preserving the appearance from the source image. As a result, a new object with the same motion will be generated in the animated video. However, it is a significant challenge if the object pose shows large-scale change. Even the most recent method failed to achieve this correctly with good visual effects. In order to solve the problem of poor visual effects in the videos with the large-scale pose change, a novel method based on an improved first-order motion model (FOMM) with enhanced dense motion and repair ability was proposed in this paper. Firstly, when generating optical flow, we propose an attention mechanism that optimizes the feature representation of the image in both channel and spatial domains through maximum pooling. This enables better distortion of the source image into the feature domain of the driving image. Secondly, we further propose a multi-scale occlusion restoration module that generates a multi-resolution occlusion map by upsampling the low-resolution occlusion map. Following this, the generator redraws the occluded part of the reconstruction result across multiple scales through the multi-resolution occlusion map to achieve more accurate and vivid visual effects. In addition, the proposed model can be trained effectively in an unsupervised manner. We evaluated the proposed model on three benchmark datasets. The experimental results showed that multiple evaluation indicators were improved by our proposed method, and the visual effect of the animated videos obviously outperformed the FOMM. On the Voxceleb1 dataset, the pixel error, average keypoints distance and average Euclidean distance by our proposed method were reduced by 6.5%, 5.1% and 0.7%, respectively. On the TaiChiHD dataset, the pixel error, average keypoints distance and missing keypoints rate measured by our proposed method were reduced by 4.9%, 13.5% and 25.8%, respectively.
Leveraging socio-technical processes to support researchers

Purpose The purpose of this project was to develop research support services that address local and external research data management (RDM) support drivers within the existing organizational culture at the University of Florida. The goal was to prompt organization change to support a campus-wide electronic lab notebook. Design/methodology/approach This project used a mixed-methods research approach to cultivate an organizational change program that support technological infrastructure to benefit researchers. The mixed-methods research involved participation action research integrated with a stakeholder approach. Findings The development of the grant proposal which was unfunded led to development of continued project goals. This project confirmed the development for support for an institution-wide electronic research notebook (ERN) solution requires adherence to the summary of five key actions for developing RDM services. Failure to complete all of the key actions engenders fragmentation culture. Research limitations/implications This project includes implications for institutions to develop grant proposals with integrated budgets for research support services of funded projects; and to use the summary of key actions for developing RDM services articulated by Jones et al. (2013) in “How to Develop RDM Services – a guide for HEIs.” Both are need to support findable, accessible, interoperable and reusable data for researchers. Practical implications This project has practical implications for higher education institutions interested in leveraging socio-technical processes to advance the role of libraries as collaborator, partner and stakeholder in developing institution-wide adoption, support and training for ERN as a research support service to RDM. Social implications This paper contributes to the body of developing literature on ERN as support services to RDM lead by academic research libraries. Originality/value This project contributed to the change in organization culture resulting in the successful collaboration between the Research Office and College of Medicine to support an institution-wide ERN technological infrastructure for one year as a pilot at a large academic research institution in the southeast USA.
Prospects for the Development of Digital CNY

After decades of development, digital currency has been recognized worldwide. But the current private cryptocurrencies in the market, including stable coins, don’t have the potential to become a unified currency. In this context, central banks of global countries have also started research on currency digitization. The People's Bank of China started research on the digital CNY in 2014, which will help the CNY escape the control of the USD and help it dominate the world currency field. After nearly ten years of development, the feasibility and development path of digital renminbi have basically formed a definite idea. However, the development prospects of the digital CNY still face issues related to the degree and method of Block Chain integration, the impact on sovereign currencies, and decentralization. This article will give a theoretical basis and policy suggestions on the development feasibility and development prospects of the digital CNY through a brief introduction and function analysis.
Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study

Abstract Patients, when in a hospital, will go through a personalized treatment scheduled for many different reasons and with various outcomes. Furthermore, some patients and/or treatments require aftercare. Identifying the need for aftercare is crucial for improving the process of the patient and hospital. A late identification results in a patient staying longer than needed, occupying a bed that otherwise could serve another patient. In this paper, we will investigate to what extent events from the first hours of stay can help in predicting the need for aftercare. For that, we explored a dataset from a Dutch hospital. We compared different methods, considering different prediction moments (depending of the amount of initial hours of stay), and we evaluate the gain in earlier predicting the need for aftercare.
Identifying the Steps in an Exploratory Data Analysis: A Process-Oriented Approach

Abstract Best practices in (teaching) data literacy, specifically Exploratory Data Analysis, remain an area of tacit knowledge until this day. However, with the increase in the amount of data and its importance in organisations, analysing data is becoming a much-needed skill in today’s society. Within this paper, we describe an empirical experiment that was used to examine the steps taken during an exploratory data analysis, and the order in which these actions were taken. Twenty actions were identified. Participants followed a rather iterative process of working step by step towards the solution. In terms of the practices of novice and advanced data analysts, few relevant differences were yet discovered.
On the Potential of Textual Data for Explainable Predictive Process Monitoring

Abstract Predictive process monitoring techniques leverage machine learning (ML) to predict future characteristics of a case, such as the process outcome or the remaining run time. Available techniques employ various models and different types of input data to produce accurate predictions. However, from a practical perspective, explainability is another important requirement besides accuracy since predictive process monitoring techniques frequently support decision-making in critical domains. Techniques from the area of explainable artificial intelligence (XAI) aim to provide this capability and create transparency and interpretability for black-box ML models. While several explainable predictive process monitoring techniques exist, none of them leverages textual data. This is surprising since textual data can provide a rich context to a process that numerical features cannot capture. Recognizing this, we use this paper to investigate how the combination of textual and non-textual data can be used for explainable predictive process monitoring and analyze how the incorporation of textual data affects both the predictions and the explainability. Our experiments show that using textual data requires more computation time but can lead to a notable improvement in prediction quality with comparable results for explainability.
An Extensive Survey on Classification of Malaria Parasites in Patients Based on Fuzzy Approaches

Malaria is one of the most potentially dangerous diseases worldwide that is disseminated by the bites of infected mosquitoes. Timely diagnosis and proper treatment are the two indispensable factors to reduce the rate of death and disease transmission. Many approaches and frameworks have already been designed to scrutinize the malaria parasites in the blood images. For better accuracy and convenience, several Fuzzy approaches have been widely used for perceiving the presence of malaria. Some of the most popular automated Fuzzy systems proposed for observing malaria microbes include neuro-fuzzy inference, fuzzy expert system, Type-2 fuzzy-based logic, and mobile-based fuzzy system. Unsupervised color segmentation, edge detection, and object recognition based on fuzzy logic in malaria-infected microscopic blood images are some of the advanced works done to obtain malaria parasites. These computational approaches assist in diagnosing the disease and avoid the time-consuming traditional method of pursuing the disorder. Much work has been done in the literature, but a theoretical survey is missing for conducting substantial research. The present chapter aims to present an extensive study of scrutinizing the presence of malaria in a patient based on observed symptoms or examining blood samples. This survey would help the medical practitioners and interdisciplinary researchers to study the summarized state-of-the-art works done in one place.
Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs

Abstract A lot of recent literature on outcome-oriented predictive process monitoring focuses on using models from machine and deep learning. In this literature, it is assumed the outcome labels of the historical cases are all known. However, in some cases, the labelling of cases is incomplete or inaccurate. For instance, you might only observe negative customer feedback, fraudulent cases might remain unnoticed. These cases are typically present in the so-called positive and unlabelled (PU) setting, where your data set consists of a couple of positively labelled examples and examples which do not have a positive label, but might still be examples of a positive outcome. In this work, we show, using a selection of event logs from the literature, the negative impact of mislabelling cases as negative, more specifically when using XGBoost and LSTM neural networks. Furthermore, we show promising results on real-life datasets mitigating this effect, by changing the loss function used by a set of models during training to those of unbiased Positive-Unlabelled (uPU) or non-negative Positive-Unlabelled (nnPU) learning.
Anti-leakage method of network sensitive information data based on homomorphic encryption

Abstract With the development of artificial intelligence, people begin to pay attention to the protection of sensitive information and data. Therefore, a homomorphic encryption framework based on effective integer vector is proposed and applied to deep learning to protect the privacy of users in binary convolutional neural network model. The conclusion shows that the model can achieve high accuracy. The training is 93.75% in MNIST dataset and 89.24% in original dataset. Because of the confidentiality of data, the training accuracy of the training set is only 86.77%. After increasing the training period, the accuracy began to converge to about 300 cycles, and finally reached about 86.39%. In addition, after taking the absolute value of the elements in the encryption matrix, the training accuracy of the model is 88.79%, and the test accuracy is 85.12%. The improved model is also compared with the traditional model. This model can reduce the storage consumption in the model calculation process, effectively improve the calculation speed, and have little impact on the accuracy. Specifically, the speed of the improved model is 58 times that of the traditional CNN model, and the storage consumption is 1/32 of that of the traditional CNN model. Therefore, homomorphic encryption can be applied to information encryption under the background of big data, and the privacy of the neural network can be realized.
Deep Neural Networks for Global Horizontal Irradiation Forecasting: A Comparative Study

In order to contribute to the integration of photovoltaic renewable energy into power system, this paper addresses the problem of forecasting solar irradiance or Global Horizontal Irradiation (GHI). The collection, adjustment and processing of meteorological data used as input is carried out, in addition various Deep Neural Networks (DNN) models are implemented and analyzed, among which are the Artificial Neural Networks (ANN) of type as Transformer, LSTM, GRU, and mixed between Convolutional ANN (CNN)-LSTM, and CNN-GRU. These ANN variants are implemented, and a comparative study are made. Finally, the results obtained show that the ANN transformer has less error in the GHI forecasting.
Penyuluhan Kesehatan Penatalaksanaan Simpel Emergency Cedera Olah Raga Pada Warga Panti Asuhan An Nisa Kecamatan Jaten Karanganyar

Simple Emergency Management (SEM) training is training on how to treat or handle people who get an accident or illness that suddenly comes before receiving help from medical personnel. can recognize how to treat patients with cases such as sports injuries that need serious treatment. The training this time was conducted for the students of the An Nisa Orphanage Jaten Karanganyar. There are 50 beneficiaries from the An Nisa Orphanage. The purpose of this counseling is to provide understanding to the residents of the orphanage regarding the first handling of emergency cases, especially in sports injuries so that the emergency situation does not spread to other things. -Things that are worse. The method used is the lecture and simulation method which is assisted by the emergency team from Kusuma Husada Surakarta, namely the Kusuma Nursing Car (KNC). After evaluating the residents, it was found that 90% of the residents understood how to take the first action in a case. Emergency.
Evaluation of Quantification Algorithms

Abstract In this chapter we discuss the experimental evaluation of quantification systems. We look at evaluation measures for the various types of quantification systems (binary, single-label multiclass, multi-label multiclass, ordinal), but also at evaluation protocols for quantification, that essentially consist in ways to extract multiple testing samples for use in quantification evaluation from a single classification test set. The chapter ends with a discussion on how to perform model selection (i.e., hyperparameter optimization) in a quantification-specific way.
The Relationship Between Mobile Money Services and Small and Medium-Sized Enterprise Growth in Bamenda, Cameroon: A Probit Model Approach

The main objective of this article is to determine the relationship between mobile money services and the growth of small and medium enterprises (SMEs). A sample of 50 SMEs was collected. The probit model was used to estimate the result. The study established that mobile money services have a positive and significant relationship with the growth of SMEs. The determinants of mobile money services observed are; experience in business management, frequency of bill payment, size of business, male entrepreneur, operation of mobile money account, frequency of purchasing airtime, distance to financial institutions, number of business branches, mobile money accounts held, secondary education, technical support, location of business, number of workers and size of capital transaction. The challenges faced by entrepreneurs in mobile money transactions observed are: wrong manipulation of account, delays, environmental complexities, and loss of money due to hawkers. The result suggests that SMEs should intensify the use of mobile money services in their daily business transactions. It is a wise step toward business expansion. Entrepreneurs of SMEs should take advantage of the drivers of mobile money services to encourage the diverse use of digital technologists. Finally, entrepreneurs should avoid risky manipulations of their mobile money gargets.
Advanced Topics

Abstract In this chapter we look at a number of “advanced” (or niche) topics in quantification, including quantification for ordinal data, “regression quantification” (the task that stands to regression as “standard” quantification stands to classification), cross-lingual quantification for textual data, quantification for networked data, and quantification for streaming data. The chapter ends with a discussion on how to derive confidence intervals for the class prevalence estimates returned by quantification systems.