Reviewerly/exHarmony huggingface dataset:
Multi-stage rotors assembly of turbine-based combined cycle engine based on augmented reality

To improve the assembly accuracy and efficiency of turbine-based combined cycle engines, we propose a multi-stage rotors assembly method based on augmented reality (AR) for turbine-based combined cycle engines. Combining the high fidelity interaction and edge computing capabilities of AR device, the real-time assembly guidance of multi-stage rotors of turbine-based combined cycle engine is realized. A new multi-stage rotors assembly mathematical model is proposed by introducing clearance errors to achieve prediction of coaxiality and perpendicularity in multi-stage rotors assembly. A point cloud registration method with equal-area constraints is proposed based on the requirement of AR virtual-real registration, combined with the triangulated graph. The experiment selected the multi-stage rotors of a simulated combination cycle engine for assembly. The experimental results showed that our AR system has an average delay of 11 ms and an average frame rate of 45fps. The accuracy of virtual-real registration is superior to current mainstream registration methods. The coaxiality prediction error and the perpendicularity prediction error for multi-stage rotors assembly is 0.5 μm and 0.4 μm, respectively. This system provides an efficient and accurate assembly plan for the rotors assembly of turbine-based combined cycle engines, meeting the actual assembly requirements on site.
Public administration as the main type of social management, the field of scientific knowledge and the field of education

The article is devoted to defining the role and importance of public administration in the world. Public administration is considered as the main type of management and branch of education, academic discipline. The content of the concept of public administration and management is revealed in detail. Possibilities and methods of public administration, forms of public administration are defined and worked out. The subject of research is the theoretical, methodological foundations and practical aspects of public administration.
ANSIEDADE NA PANDEMIA: UM ESTUDO SOBRE OS IMPACTOS NA SAÚDE MENTAL DE PROFISSIONAIS DA SAÚDE DURANTE O CONTÁGIO DE COVID-19 NO BRASIL

The COVID-19 pandemic represented not only a health emergency, but a mental health catastrophe. The aim of the study was to investigate the occurrence of anxiety symptoms in health professionals during the COVID-19 pandemic, identifying and describing the symptoms experienced during this period. This is a quantitative survey, where 102 participants answered an online questionnaire and the data were tabulated in an Excel spreadsheet and analyzed using the STATA® 13 software (Stata Corporation, College Station, TX, USA). As a result, it was possible to notice that the prevalence of anxiety in the present study sample was 74.5%, with no statistically significant differences between genders. About age, a specific age group with a higher risk of developing anxiety symptoms was not identified. The factors significantly associated with anxiety by univariate regression were: depression (OR=3.21, 95% CI=1-10.26), Insomnia (OR=3.45, 95% CI-1.33 - 8.93), Annoyance with too little or too much information (OR =3.08 95% CI=1.23 - 7.73), Irritability (OR= 4.45, 95% CI=1.4 - 14.17) and fear of returning to work (OR=5, 95% CI=1.38 - 18.13). The only factor associated with anxiety by multivariate regression in this study was fear of returning to work (OR=3.84 95% CI=0.99 - 14.78). Understanding the factors closely related to anxiety in health professionals arising from the pandemic situation is extremely important so that emotional support measures are adopted in order to mitigate the impacts arising from the COVID-19 pandemic, reducing the risk of mental illness which will occur with the prolonged absence, disability and psychological suffering of health professionals. &nbsp
Pengembangan Media Game Edukasi Where is Myhometown Berbasis Android Materi Keragaman Budaya di Indonesia

The majority of children aged 9 years and over are very happy to play online games, thus making children lazy to open books to study. This study aims to develop an Android-based Where Is Myhometown Educational Game Media in Class IV Civic Education Lessons on Cultural Diversity in Indonesia. This research was carried out at SDN Cipinang Melayu 10 Pagi, in the even semester 2021/2022 academic year. This research has been tested by 3 validators, namely: media experts, material experts, and education experts. This media game has also been tested through a small group test containing 10 students and a large group test containing 20 students where the student is a fourth grade student at SDN Cipinang Melayu 10 Pagi. This study uses the Research and Development (R&amp;D) method by applying the ADDIE model and the data collection technique is in the form of a questionnaire using a Likert scale. This educational game media where is myhometown succeeded in making it easy for students to understand the variety of Indonesian culture and its origins. By using this educational game, students become happier in learning Indonesian culture.&#x0D; &#x0D; Keywords: ADDIE, educational game
The Wild Probes: Towards a Collection of Hybrid Tools for Situated, Caring &amp; Playful Co-design within the Forest

The Wild Probes (WPs) are a set of hybrid tools for designers and researchers to facilitate multi-stakeholder co-design engagements within the forest. They support situated forestry future-making by helping the participants of a co-design process pay attention to, reflect on, ideate around, and document their forestry experiences in ways that can inspire contextually grounded forest-related ideation. Here we present the design and early use of the first iteration of the WPs. &#x0D; The WPs extend existing tools available to designers by adapting their underlying mechanisms to the idiosyncratic character of the forest. We designed them building on recent research on the methodological underpinnings of (co-)designing for and from the forest. The WPs run on affordable, widely accessible electronics and can easily be built with basic DIY skills and equipment. We thus invite others to replicate, enhance, and repurpose them. Overall, here we contribute a first step towards creating a collection of tools to support co-design that is situated in the forest. We hope other designers will find our proposals useful and contribute to growing the collection by creating new WPs of their own.
A Study of Protean Career Development Process for the Middle-Aged Women: with focused on Career Professional Instructors at Elementary or Secondary Schools

Purpose: This study aims to explore the process of protean career development for the middle-aged woman, working as part-time career professional instructors at elementary or secondary schools. Method: A qualitative case study using the ground theory was adopted. The 11 middle aged women working as career professional instructors at schools by the commission of the I Office of Education were selected and in-depth interviews were conducted. The interview results were analysed according to the paradigm model of ground theory. Results: The focal phenomenon was the incessant and self-regulated activities for growth resulting the recognition and establishment of self-identity. A sense of crisis led to the development of job skills necessary for their jobs through various types of action/interaction strategies depending on their points of values. Supportive relationships contributed to overcome job conflicts from unstable job conditions. Conclusion: Institutional supports and policies must be considered to facilitate the development of protean careers for the middle-aged women career professional instructors.
Excitability in a PhC Nanolaser with an Integrated Saturable Absorber

Although machine learning (ML) algorithms are already applied in many fields (e.g. language recognition, temporal series prediction) using software computed on “Von Neumann” architectures, numerous technological advances (e.g. robotics, autonomous driving) require dedicated hardware. Integrated optics constitute a highly promising platform that could be harnessed for achieving portable ML chips with unprecedented power-efficiency and bandwidth [1]. Despite recent observations of ML key functionalities using integrated Silicon photonics, such as reconfigurable matrix multiplication [2] and nonlinear activation functions [3], excitability observation, the spiking mechanism of our biological neurons, remains incomplete in all-integrated systems; they are based on opto-thermal (slow) effects [4] that drastically limits its applicability in ML systems with high bandwidth requirements. Here, we use InP-based photonic crystal nanocavities heterogeneously integrated on top of a Silicon on Insulator (SOI) waveguide to demonstrate the first all-integrated (fast) excitable nanolaser.
TERNATE MALAY CODE MIXING IN ENGLISH TEACHING LEARNING PROCESS AT SMA NEGERI 2 KOTA TERNATE

The objective of this research was to determine the types of Ternate Malay code mixing used by teachers and the reasons for its use in SMA Negeri 2 kota Ternate. This research use a descriptive qualitative method using a descriptive approach. The research subjects consisted of English teachers in grades X and XI. The results of this study indicated that teachers used three types of code mixing, namely inner code mixing, outer code mixing, and hybrid code mixing. The it was also found that three reasons for using code mixing, include the teacher's desire to explain and translate the material, the teacher lacks English vocabulary and it has become a habits.
A Deep Natural Language Inference Predictor Without Language-Specific Training Data

In this paper we present a technique of NLP to tackle the problem of inference relation (NLI) between pairs of sentences in a target language of choice without a language-specific training dataset. We exploit a generic translation dataset, manually translated, along with two instances of the same pre-trained model—the first to generate sentence embeddings for the source language, and the second fine-tuned over the target language to mimic the first. This technique is known as Knowledge Distillation. The model has been evaluated over machine translated Stanford NLI test dataset, machine translated Multi-Genre NLI test dataset, and manually translated RTE3-ITA test dataset We also test the proposed architecture over different tasks to empirically demonstrate the generality of the NLI task. The model has been evaluated over the native Italian ABSITA dataset, on the tasks of Sentiment Analysis, Aspect-Based Sentiment Analysis, and Topic Recognition. We emphasise the generality and exploitability of the Knowledge Distillation technique that outperforms other methodologies based on machine translation, even though the former was not directly trained on the data it was tested over.
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond

Federated learning (FL) is a framework for training machine learning models in a distributed and collaborative manner. During training, a set of participating clients process their data stored locally, sharing only the model updates obtained by minimizing a cost function over their local inputs. FL was proposed as a stepping-stone towards privacy-preserving machine learning, but it has been shown vulnerable to issues such as leakage of private information, lack of personalization of the model, and the possibility of having a trained model that is fairer to some groups than to others. In this paper, we address the triadic interaction among personalization, privacy guarantees, and fairness attained by models trained within the FL framework. Differential privacy and its variants have been studied and applied as cutting-edge standards for providing formal privacy guarantees. However, clients in FL often hold very diverse datasets representing heterogeneous communities, making it important to protect their sensitive information while still ensuring that the trained model upholds the aspect of fairness for the users. To attain this objective, a method is put forth that introduces group privacy assurances through the utilization of $d$-privacy (aka metric privacy). $d$-privacy represents a localized form of differential privacy that relies on a metric-oriented obfuscation approach to maintain the original data's topological distribution. This method, besides enabling personalized model training in a federated approach and providing formal privacy guarantees, possesses significantly better group fairness measured under a variety of standard metrics than a global model trained within a classical FL template. Theoretical justifications for the applicability are provided, as well as experimental validation on real-world datasets to illustrate the working of the proposed method.
An Efficient Task Scheduling in a Cloud Computing Environment using Hybrid Swarm Optimization

Information technology (IT) services are available on demand over the Internet under the current computing standard known as cloud computing. A work is mapped with an available resource in the cloud environment to get a good result. Cloud computing has emerged as a transformative technology that provides on-demand access to a shared pool of computing resources over the internet. Effective task scheduling plays a crucial role in optimizing resource utilization and improving overall system performance in cloud environments. According to a server's workload capacity, tasks are distributed across its virtual machines (VMs) via task scheduling. The server's workload is planned out to reduce traffic and wait times. The best algorithm currently in use to schedule a work to an existing resource in a cloud setting is particle swarm optimization (PSO). PSO schedules the task for an available resource to cut down on computational costs. In order to address job scheduling challenges in the cloud environment, a hybrid swarm optimizations (HSO) algorithm—a combination of PSO and salp swarm optimization (SSO)—is suggested in this paper. The hybrid algorithm leverages the strengths of both PSO and SSO to achieve better optimization results. The proposed method is evaluated using the CloudSim simulator and compared with traditional scheduling algorithms. Simulation results demonstrate the superiority of the hybrid PSO-SSO algorithm in terms of makespan time, cloud throughput, and task execution efficiency. The findings suggest that the hybrid approach can significantly enhance resource utilization and performance in cloud computing environments.
Knowledge Management and Cryptocurrencies: review and reflection

In this paper we try to reflect on how one of the financial phenomena of the last twenty years, namely cryptocurrencies, has been analysed by the scientific community of the Knowledge Management (KM) field. The issue is relevant because the raise of cryptocurrencies as an economic asset has been occurring in a time in which KM gained social prominence. And also because the evolution of cryptocurrencies should be also related to knowledge about their own value. Within this context, we present a literature review on papers that exist in the SCOPUS database about cryptocurrencies and KM. After analysing those papers, the general idea is that KM is very far away from the cryptocurrency phenomenon; the reason may be that it is very difficult to use the more common models available on KM to analyse cryptocurrencies; also the economic and social agents that might be interested in KM are not those who invest in cryptocurrencies; finally, the data available on cryptocurrencies are mostly speculative, and it is very difficult to make any scientific study on them. These conclusions may be of interest for the KM community at large, because they indicate a new subfield of research, and for practitioners, because they mean that there is not much science in the evolution of cryptocurrencies themselves. Finally, for policymakers, the findings mean the expanding the possibility of use of cryptocurrencies in societies may be extremely risky given their volatility and the lack of precise scientific knowledge about them. The paper is original because it relates to concepts that have only very seldom and scarcely put together.
CLARK: Building Conversational Intelligence for Knowledge Management in the Space Domain

This paper presents the case study of the CLARK (Captured Lessons applied for the Reuse of Knowledge) project which is an evolution of the Lessons Learned (LL) portal at the European Space Agency (ESA). The SCARLET research activity has been a key for-runner to this project on an academic research level. It has provided us with important insights into the architecture of the system design. A knowledge graph (KG) has been developed to (re)search (for) knowledge from different angles through the established relationships by combining and structuring data from many sources. Additionally, a chatbot is trained to provide a conversational intelligence experience on top of the data provided by the different Knowledge Management (KM) activities at ESA. Hence, CLARK project enables to retrieve, search, or provide knowledge for the employees across ESA through different interfaces connected to the core systems. In this paper, we also report some findings from the initial testing which indicate very promising results in terms of user engagement and usability of CLARK system and the overall approach. The paper also discusses current and future challenges.
Anomaly Detection Method based on Multi-Source Subsequence Multi-View Fusion

The integration of multiple sensors in industrial systems and subsequent anomaly detection based on the sensor-collected data has emerged as one of the most popular applications of the Internet of Things. Therefore, it is highly significant to conduct anomaly detection based on multi-source subsequence data. However, existing research either deals with anomaly detection for multi-source data fusion or anomaly detection for subsequences. In this paper, we propose a novel Anomaly Detection method based on multi-source subsequence multi-View data that overcomes the limitations of the existing methods. Specifically, we introduce an improved shape-based subsequence mapping method for the representation of multi-source subsequences, followed by a multi-view based feature fusion matrix construction method. Finally, we propose an anomaly scoring method based on property attribute fusion, which is the first of its kind to introduce a source importance factor that takes into account the importance of different data sources for adjusting the impact weight. Our experimental results demonstrate that the proposed method outperforms traditional anomaly detection methods.
METHODICAL EVALUATION OF HEALTHCARE INTELLIGENCE FOR HUMAN LIFE DISEASE DETECTION

Event intelligence for early diseases detection is highly demanded in current era and it requires reliable technology-oriented applications. Trusted emerging technologies play a vital role in modern healthcare systems for early diagnoses of different medical conditions because it helps to speed up the treatment process. Despite the enhancement of current healthcare systems, robust diagnosis of different type of diseases for intra-patients (outside of hospital settings) is still considered as a difficult task. However, the continuous evolution of trusted technologies in health sectors narrate the reboot process which could upgrades the healthcare service provision as the trusted next generation health units. In order to assist the healthcare providers to carry out early diseases’ detection for intra-patient clients, we designed this systematic review. We extracted 40 studies from the databases i.e. IEEE Xplore, Springer, Science direct and Scopus, from March 2016 and February 2021, and we formulated our research questions based on these studies. Subsequently, we rectified these studies using two filtration schemes namely, inclusion-omission policy and quality assessment, and as a result, we obtained 19 studies which successfully mapped our defined research questions .We found that these 19 studies clearly highlighted the different trusted architecture of internet of things, mobile cloud computing and machine learning, that are significantly beneficial to diagnose medical conditions for the intra-patient clients such as neurological diseases, cardiac malfunctions and other common diseases.
Blockchain Adoption

This chapter aims to be an introductory guide in understanding how blockchain technology works, including its terminology and characteristics that make it different and revolutionary. It also discusses the cases for which its implementation could be beneficial/of interest for society and people, mostly related to the trust aspects of its use. This is accomplished through a literature and documentary review of papers, business cases, and regulations implemented, particularly in Mexico. The findings seem to point towards a social benefit for economies, as long as the foundations of a legislation that regulates its applications and possible impacts and misuse is established. Finally, this chapter looks at the need to establish a reference framework that helps those who wish to adopt this technology for the creation of value in different contexts.
DETERMINATION OF ANALYTICAL DEPENDENCIES FOR VARIANCES OF SAMPLE COEFFICIENTS OF ASYMMETRY AND KURTOSIS OF TYPICAL APPROXIMATING DISTRIBUTIONS

The article is devoted to the issues of determining analytical dependencies for variances of sample characteristics of statistics of coefficients of asymmetry and kurtosis of typical distributions. It is shown that the values of the parameters of the exact sample distributions of the coefficients of asymmetry and kurtosis are strongly correlated with the type of the main distribution of a random variable or a random process. Schemes of statistical conclusions of analytical solutions are given. Graphical solutions are given. Specific dependences and graphs for the variances of the statistics of the coefficients of asymmetry and kurtosis of typical unimodal distributions are given.
A Concurrent Federated Reinforcement Learning for IoT Resources Allocation With Local Differential Privacy

Resource allocation in an edge-based Internet of Things (IoT) systems can be a challenging task, especially when the system contains many devices. Hence, in recent years, scholars have devoted some attention to designing different resource allocation strategies. Among these strategies, reinforcement learning is considered to be one of the best methods for maximizing the efficiency of resource allocation schemes. In a typical reinforcement learning scheme, the edge hosts would be required to upload their local parameters to a central server. However, this process has privacy implications given some of the data processed by the edge hosts is likely to be highly sensitive. To tackle this privacy issue, we developed a concurrent joint reinforcement learning method based on local differential privacy. Our approach allows the edge host to add noise during local training to preserve privacy, and to make joint decisions with the central server to devise an optimal resource allocation strategy. Experiments show that this approach yields high performance while preserving the privacy of the edge hosts.
Entangling Quantum Memories via Heralded Photonic Bell Measurement

We evaluate the quality of entangled states generated on quantum network links based on entanglement swapping. Network non-idealities in conjunction with physi-cal memory decoherence models are incorporated to demonstrate encoding trade-offs and limitations for quantum networks.
Noise Modeling for Entanglement-Swapping Quantum Networks

Future quantum networks will require high-fidelity entanglement swapping at fast rates to connect distant nodes. We present a generalizable network model for memoryless entanglement swapping accounting for noise from imperfect sources, links, and detectors.