Reviewerly/exHarmony huggingface dataset:
Research on Conductive Polymer Materials

With the continuous improvement of the national science and technology level, some advanced high-tech has been the focus of the country. It has played an important role in enhancing the lives of the people and improving their living habits. In particular, the technical means represented by Internet technology have completely subverted the past lifestyle. But for the development of the entire country, it is necessary to strengthen the research and development of industry and new material science, especially the research of conductive polymer materials. It is necessary to further strengthen capital investment and scientific and technological research and ensure that the entire development process is able to obtain large-scale applications of new materials. This article takes the research of conductive polymer materials as the topic through a comprehensive introduction to its concept, significance and effective application of polymer conductive materials. It aims to bring some references.
Unpacking Ethereum

Ethereum is an open source, decentralized blockchain platform with computational capabilities that reconstructs an elementary currency exchange into a transfer of value between users via a scripting language. Ethereum is widely recognized as a successor to the Bitcoin protocol, generalizing the original ideas and enabling a more diverse array of applications to be built on top of the blockchain technology. Ethereum has two essential components. First, there is a Turing-complete virtual processor that can load resources and execute scripts called the Ethereum Virtual Machine (EVM). The second component is a token of value called ether, which is the currency of the network and is used for user-to-user transactions or compensation to miners of the network. In this chapter, we begin our journey with an overview of Ethereum’s architecture in comparison to Bitcoin, focusing on the EVM and Turing-completeness properties. Following the architecture section, there is a short discussion of the accounts model in Ethereum and account representation with Merkle-Patricia Trees. This will lead us to the topics of global state representation in Ethereum, account storage, and gas, which is a spam-prevention mechanism in the network. Then, we deconstruct the notion of a smart contract enabled by EVM, the security concerns revolving around sandboxing executable code, and how the EVM pushes executable code (bytecode) to the blockchain. After that, we provide an introduction to Solidity and Vyper, two programming languages used for writing smart contracts in Ethereum. We explore the syntax of Solidity and Vyper, as well as the popular integrated development environments (IDEs) being used, and provide a brief list of key developer resources. Next, we focus on the World Computer model proposed in Ethereum and introduce supporting decentralized technologies such as IPFS and Whisper. Then, we look at the state of decentralized apps (DApps) along the publishing platform called Mist available in Ethereum. This allows us to transition into talking about the Layer 2 updates to Ethereum—the major technical focus of this chapter and the Ethereum ecosystem in 2020. Finally, we conclude the chapter with a brief discussion of the enterprise side. Here, we introduce a particularly noteworthy development of Blockchain-as-a-Service (BaaS) deployed on the Azure cloud by Microsoft.
Discursive delegitimisation of homosexuality on Chinese social media

This article contributes to the understanding of public representations of homosexuality in China by focusing on the case of a homophobic textbook. College student Xixi sued Jinan University Press (JUP) in 2017 for classifying homosexuality as a psychosexual disorder. Three years later, a Chinese court dismissed Xixi’s lawsuit against the allegedly homophobic textbook published by JUP. The ruling elicited responses on Chinese social media that demonstrated the polarisation of public opinion regarding homosexuality. This article investigates discursive representations of homosexuality in online space by analysing the public discourse surrounding this problem. Using van Leeuwen’s discursive delegitimation strategies (i.e., authorisation, moral evaluation, rationalisation, and mythopoesis), 496 comments posted on Sina Weibo were employed and subjected to discourse analysis. According to our findings, these strategies contribute to public opposition to homosexuality, portrayed as unhealthy, infertile, disruptive, and corrosive. The article concludes by discussing the emerging sociocultural factors on Sina Weibo that influence the anti-homosexuality attitudes of Weibo users.
An Ensemble Approach for Intra-Hour Forecasting of Solar Resource

Solar resource forecasting is an essential step towards smart management of power grids. This study aims to increase the performance of intra-hour forecasts. For this, a novel ensemble model, combining statistical extrapolation of time-series measurements with models based on machine learning and all-sky imagery, is proposed. This study is conducted with high-quality data and high-resolution sky images recorded on the Solar Platform of the West University of Timisoara, Romania. Atmospheric factors that contribute to improving or reducing the quality of forecasts are discussed. Generally, the statistical models gain a small skill score across all forecast horizons (5 to 30 min). The machine-learning-based methods perform best at smaller forecast horizons (less than 15 min), while the all-sky-imagery-based model performs best at larger forecast horizons. Overall, for forecast horizons between 10 and 30 min, the weighted forecast ensemble with frozen coefficients achieves a skill score between 15 and 20%.
Multi-agent Verification and Control with Probabilistic Model Checking

Probabilistic model checking is a technique for formal automated reasoning about software or hardware systems that operate in the context of uncertainty or stochasticity. It builds upon ideas and techniques from a diverse range of fields, from logic, automata and graph theory, to optimisation, numerical methods and control. In recent years, probabilistic model checking has also been extended to integrate ideas from game theory, notably using models such as stochastic games and solution concepts such as equilibria, to formally verify the interaction of multiple rational agents with distinct objectives. This provides a means to reason flexibly about agents acting in either an adversarial or a collaborative fashion, and opens up opportunities to tackle new problems within, for example, artificial intelligence, robotics and autonomous systems. In this paper, we summarise some of the advances in this area, and highlight applications for which they have already been used. We discuss how the strengths of probabilistic model checking apply, or have the potential to apply, to the multi-agent setting and outline some of the key challenges required to make further progress in this field.
Investigating the Toe Factors Influencing IOT Adoption and SMEs Performance in Pakistan: The Mediating Role of Organization Resilience

The development of the fourth industrial revolution (Industry 4.0) suggests significant changes in the organization's dynamics. Despite its potential gains, IoT has been widely and effectively implemented by businesses, especially small and medium-sized enterprises (SMEs). In terms of IoT adoption, many developing nations are very far behind. Similarly, IoT adoption in the SME sectors of Pakistan remains in its inception. The research will apply the TOE framework, and its theoretical basis is the resource-based perspective (RBV). This study intends to investigate the impact of organizational resilience in mediating the link between IoT adoption and the performance of SMEs in manufacturing sectors. The data for this research will be collected using a questionnaire with a seven-point Likert scale from Pakistani SME owners, executives, and middle managers. On returned surveys, the Partial Least Squares Structural Equation Modelling (PLS-SEM) approach will be used. The empirical findings of this study will give managers and practitioners with helpful theoretical and practical insights for understanding the underlying variables for the effective adoption of IoT in the SME sector to improve company performance.
A Method of Analyzing Flight Exceedance Events Based on Association Rule and QAR Data

Flight Operation Quality Assurance (FOQA) is a crucial approach to improve safety by monitoring exceedance events. However, an exceedance event implies the monitoring parameters of flight data exceed the standard, and is determined by multiple causes such as pilots’ operations and environmental conditions. In order to provide support for the judgment of pilots’ exceedance behaviors, this paper utilized the Apriori algorithm to mine association rules in exceedance events and their correlations with environmental conditions (e.g., season and airport). Results revealed some critical associations, such as the speed exceedance during approach at an altitude of 500-50ft is more likely to result in speed exceedance during approach at an altitude below 50ft. Our findings are expected to help airlines consider exceedance events more reasonably, as well as identify the environmental factors associated with exceedance events.
Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears

Abstract Background Scarcity of annotated image data sets of thin blood smears makes expert-level differentiation among Plasmodium species challenging. Here, we aimed to establish a deep learning algorithm for identifying and classifying malaria parasites in thin blood smears and evaluate its performance and clinical prospect. Methods You Only Look Once v7 was used as the backbone network for training the artificial intelligence algorithm model. The training, validation, and test sets for each malaria parasite category were randomly selected. A comprehensive analysis was performed on 12 708 thin blood smear images of various infective stages of 12 546 malaria parasites, including P falciparum, P vivax, P malariae, P ovale, P knowlesi, and P cynomolgi. Peripheral blood samples were obtained from 380 patients diagnosed with malaria. Additionally, blood samples from monkeys diagnosed with malaria were used to analyze P cynomolgi. The accuracy for detecting Plasmodium-infected blood cells was assessed through various evaluation metrics. Results The total time to identify 1116 malaria parasites was 13 seconds, with an average analysis time of 0.01 seconds for each parasite in the test set. The average precision was 0.902, with a recall and precision of infected erythrocytes of 96.0% and 94.9%, respectively. Sensitivity and specificity exceeded 96.8% and 99.3%, with an area under the receiver operating characteristic curve &amp;gt;0.999. The highest sensitivity (97.8%) and specificity (99.8%) were observed for trophozoites and merozoites. Conclusions The algorithm can help facilitate the clinical and morphologic examination of malaria parasites.
A probabilistic modeling and evolutionary optimization approach for serverless workflow configuration

Abstract Serverless computing has nowadays become a mainstream paradigm to develop cloud‐native applications owing to its high scalability, ease of usage and cost‐effectiveness. Nevertheless, because of its poor infrastructure transparency, two main challenges emerge when users migrate their applications to a serverless platform: the lack of an effective analytical model for performance and billing, and the trade‐off problem between them. In this paper, we formally define a serverless workflow and introduce the concept of execution instances. Based on them, a probabilistic performance and cost evaluation model is built to obtain their expected values for an input serverless workflow. Then, we design a tailored evolutionary optimization algorithm called EASW to tackle budget‐constrained performance optimization and performance‐constrained cost optimization problems. Extensive experiments were carried out to test the proposed model and optimization algorithm on AWS Lambda. Results reveal that our model can achieve an accuracy over 98% and EASW can yield a better memory configuration solution than existing methods for constrained optimization.
Enhancing Learning Process by Applying Cooperative Learning Supported with Augmented Reality Environment

This study aims to examine the influence of Augmented Reality (AR) on Cooperative Learning (CL) and Students' Achievement (SA) within the context of education. This study employed a survey-based research design to investigate the proposed connections between constructs, using a two-step methodology to empirically examine the hypothesized relationships. The data collection process involved the creation of a meticulously designed, structured questionnaire, specifically targeting students at the Institute of Public Administration in KSA. A pilot test was conducted with the assistance of three experienced instructors, and a remarkable response rate of 67% was achieved with 81 completed questionnaires, of which 67 were deemed usable for analysis. To test the hypothesized associations, the cutting-edge partial least squares (PLS) modeling approach was employed, ensuring accurate and reliable results. The learning process begins with teachers explaining the new learning process and students being grouped based on cooperative learning strategies. Students will be asked to install the ARLOOPA application on their mobile devices and given printed cards to scan with their mobile devices. The findings provide compelling evidence supporting the hypotheses, indicating that the use of AR has a positive effect on both CL and SA. Furthermore, the study confirms the reciprocal relationship between CL and SA, suggesting that CL implementation fosters improved student achievement. Overall, this study contributes to the existing literature and provides valuable insights into the application of AR to enhance cooperative learning and student achievement.
Neural Models for Factual Inconsistency Classification with Explanations

Factual consistency is one of the most important requirements when editing high quality documents. It is extremely important for automatic text generation systems like summarization, question answering, dialog modeling, and language modeling. Still, automated factual inconsistency detection is rather under-studied. Existing work has focused on (a) finding fake news keeping a knowledge base in context, or (b) detecting broad contradiction (as part of natural language inference literature). However, there has been no work on detecting and explaining types of factual inconsistencies in text, without any knowledge base in context. In this paper, we leverage existing work in linguistics to formally define five types of factual inconsistencies. Based on this categorization, we contribute a novel dataset, FICLE (Factual Inconsistency CLassification with Explanation), with $$\sim $$ 8K samples where each sample consists of two sentences (claim and context) annotated with type and span of inconsistency. When the inconsistency relates to an entity type, it is labeled as well at two levels (coarse and fine-grained). Further, we leverage this dataset to train a pipeline of four neural models to predict inconsistency type with explanations, given a (claim, context) sentence pair. Explanations include inconsistent claim fact triple, inconsistent context span, inconsistent claim component, coarse and fine-grained inconsistent entity types. The proposed system first predicts inconsistent spans from claim and context; and then uses them to predict inconsistency types and inconsistent entity types (when inconsistency is due to entities). We experiment with multiple Transformer-based natural language classification as well as generative models, and find that DeBERTa performs the best. Our proposed methods provide a weighted F1 of $$\sim $$ 87% for inconsistency type classification across the five classes. We make the code and dataset publicly available ( https://github.com/blitzprecision/FICLE ).
Library and information sphere through the prism of scientific journals

The authors analyze scientific journals that are relevant for the publication of works by library and information researchers. The study methodology is based on the selection of peer-reviewed journals indexed in the Russian Science Citation Index (RSCI), embracing various thematic headings in the area of library and information studies. The list of journals comprises 38 titles within the following RSCI headings: Librarianship, Library Studies, Information Studies, Economics, Economic Studies, Sociology, and Science Studies. The presence/absence of the journal in the RSCI, RSCI, the White List and in the List of the Higher Attestation Commission with the indication of significance/importance category assigned to the journal (Q1, Q2, Q3) is shown. The findings of the study demonstrate that researchers in the library and information sphere have a relatively little choice for publishing their papers in scientific professional journals that meet all the requirements. The authors also examine thematic open access platforms (aggregators) and provide examples. They discuss the Portal for the Periodicals in Librarianship, Bibliography and Book Studies. The platform approach expands visibility of journals, especially of the regional ones, which contributes to the scientific research and science promotion.
Detection of types cyber-bullying using fuzzy c-means clustering and xgboost ensemble algorithm

In this study a neural network model (XGB_CTD) that will prediction which type of bullying the users may expose to, through dataset gained by the cyberbullying scale applied to the young internet users is formulated. Extreme Gradient Boosting (XGboost) algorithm, one of the ensemble learning methods is used in this method. There while this model contains 13 input parameters taken from the scale, there exist one output parameter classified one of the 9 outputs. The reliability of the data set obtained through survey is confirmed by statistical methods. Data set has been fragmented with Fuzzy C-Means (FCM) which is one of fuzzy clustering algorithms. Hyper-parameters for the maximum efficiency of the model training have been defined as model, learning and boosting method. Independent variables in data set have been scaled through standard normalization. As a result, the model has yielded % 91,75 accuracy rate in prediction of the classification as 9 different cyberbullying types. The same data set has been trained by different machine learning algorithms. It is seen that the proposed model has reached the highest accuracy when compared to the conventional machine learning algorithms. This study aims at prediction cyberbullying through the proposed model including different questions without claim by the young users as they were bullied. Similarly, type of the cyberbullying will also be able to be estimated by the help of internet using habits of the young users. Therefore, it is thought that the young can be prevented from experiencing psychological pressure or digital life fear.
Blockchain and Cryptocurrency: A Bibliometric Analysis

Blockchain is a technology that collects data in a blockchain and allows transactions securely and transparently. It is used in many fields. Bibliometric publications examining detailed research trends in blockchain and cryptocurrency are still rare, but one bibliometric report did include research on the topic. The present work is filling in the void that eludes the study of research in the blockchain area. In the current work, we have included a wide range of blockchain and cryptocurrency research efforts to date. This study used the Scopus database to extract 2,045 articles from 2014 to 2021. The goal was to examine blockchain and cryptocurrency growth in volume, trend, global distribution, significant journals, pioneering authors, leading nations, and dominant sectors. This study highlights many aspects of worldwide blockchain and cryptocurrency research by researchers from all over the world. This research also studies how consensus decision-making and its processes are used. This study highlights many aspects of global blockchain and cryptocurrency research. This study also examines their use and consensus methods.
Simultaneous three-dimensional deformation measurement using tri-wavelength and monochrome camera imaging with a pyramid prism

What we believe to be a new electronic speckle pattern interferometry (ESPI) configuration is being developed for simultaneous three-dimensional deformation measurements. In this ESPI system, two pairs of symmetrical illuminating arrangement with dual-wavelength lights were used to independently sense two in-plane deformation components, one Michelson interferometer-based set illuminating with the other wavelength light was utilized to measure out-of-plane deformation. The color speckle interferogram was split into four sub-patterns by a prism, three of them were filtered by three different bandpass dichroic filters and recorded by one monochrome camera. Micro-rotation testing work firstly verifies the validation of the proposed phase-shifting device. Three-dimensional deformation information was simultaneous obtained by using temporal phase-shift method. All strain components related to the specimen surface deformation were further determined by numerical differential. The experimental results of a tested specimen were excellently consistent with those of FEM simulation, which verified the validation and feasibility of the proposed ESPI system for measuring 3D deformation.
Trajectory planning technique based on curved-layered and parallel contours for 6-DOF deposition

The manufacture of solids using material deposition has boomed in recent years, and its use has grown steadily, generating challenges to improve printing. In addition, new mechanisms of six degrees of freedom have been designed focused on manufacturing parts, creating problems such as their trajectory planning. Therefore, this paper presents a new path-planning methodology based on the contour-parallel pattern and, through propagation techniques and geodesic flattening, transforms curved spaces into flat contours and vice versa. Furthermore, this methodology generates continuous trajectories for manufacturing parts for mechanisms of six degrees of freedom, focused on improving the surface quality and the quantity of deposited material, eliminating unwanted effects such as ladder and stringing, which usually occur in 3D printing.
Childhood obesity in Singapore: A Bayesian nonparametric approach

Overweight and obesity in adults are known to be associated with increased risk of metabolic and cardiovascular diseases. Obesity has now reached epidemic proportions, increasingly affecting children. Therefore, it is important to understand if this condition persists from early life to childhood and if different patterns can be detected to inform intervention policies. Our motivating application is a study of temporal patterns of obesity in children from South Eastern Asia. Our main focus is on clustering obesity patterns after adjusting for the effect of baseline information. Specifically, we consider a joint model for height and weight over time. Measurements are taken every six months from birth. To allow for data-driven clustering of trajectories, we assume a vector autoregressive sampling model with a dependent logit stick-breaking prior. Simulation studies show good performance of the proposed model to capture overall growth patterns, as compared to other alternatives. We also fit the model to the motivating dataset, and discuss the results, in particular highlighting cluster differences. We have found four large clusters, corresponding to children sub-groups, though two of them are similar in terms of both height and weight at each time point. We provide interpretation of these clusters in terms of combinations of predictors.