Reviewerly/exHarmony huggingface dataset:
An Optimization of Makespan, Energy Consumption, and Load Balancing on The Task Scheduling in Cloud Computing using Particle Swarm Optimization (PSO)

Cloud computing is widely used resource sharing computational technology to provide fast, reliable, and scalable computational process for organizations and companies without the need to build and maintain their own server. The research area about cloud computing is dynamic and versatile. One may have concern on the privacy, security, networking, optimization, etc. Due to huge demand for cloud computing, it creates several problems such as makespan, energy consumption, and load balancing. Task scheduling is one of the technologies that have been applied to solve those objectivities. However, task scheduling is one of the well-known NP-hard problems, and it is difficult to find the optimum solution. In order to solve this problem, previous studies have utilized meta-heuristic method to find the best solution based on the solution spaces. This study proposed Particle Swarm Optimization (PSO) to solve the multi-objective task scheduling to achieve the optimum solution. The effectiveness of the proposed algorithm will be compared with Genetic Algorithm (GA), Clonal Selection Algorithm (CSA), and Bat Algorithm (BA). This study converts three objectivities into single objectivity optimization with each objectivity act as variable assigned with weight that present its priority and has implemented those meta-heuristics. The simulation result from ten data set shows that PSO able to outperform GA, CSA, and BA especially for makespan and energy consumption without the cost of algorithm duration since PSO has fast convergence rate compare to the other three algorithms and making it a good choice for dynamic task scheduling in data center cloud computing where the algorithm duration is one of important factor
Informing Collection Development through Citation Examination of the Civil Engineering Research Literature

Librarians must scrutinize collection development decisions to ensure that patron use of library materials meets expectations based upon institutional cost and effort to acquire, organize, and provide access to these materials.Some librarians have studied reference citation patterns within engineering as a tool for collection development.Because current commercial citation indexing tools focus on analysis by author and subject, gathering different types of citation information of interest to librarians can prove time-consuming.This paper presents a study that builds upon field-spanning and subfield-specific journal citation studies by Musser and Conkling 8 and Musser 9 , a study of theses and dissertations by Eckel 3 , and in particular a study of civil engineering theses and dissertation citations by Kirkwood 7 .Kirkwood analyzed citations by format of the cited material, finding that 40% of referenced sources in these theses and dissertations in civil engineering were grey literature, and noted that master's students cited grey literature almost twice as frequently as doctoral students at her institution.This paper seeks to establish whether format use within citations in the civil engineering literature differs appreciably from the patterns reported in earlier studies.In addition, the current study proposes a flexible data organization method that should allow for relatively straightforward re-use of the data in future, as yet undetermined, analyses.
Constructions with parataxis and homogeneous parts in poetical text

The work considers peculiarities in conducting of automated syntactic analysis (ASA) on basis of poetical texts and as result studying the features of linear structure of Ukrainian texts, in particular, texts with constructions with parataxis and homogeneous parts. The process of text analysis includes two stages: the first stage starts with automated syntactic analysis of hierarchical structure of text, which finally builds a dependency tree. On the second stage the graph received as result of automated analysis specifies information on type or model of a sentence, in particular, with available constructions with parataxis homogeneous parts. The purpose of the work from linguistic standpoint is the analysis of parataxis/homogeneous parts in poetical texts and their relations to the content. The study of texts (1600 sentences) showed that constructions with parataxis can build three groups of relations with: homogenous parts, non-homogenous and a combination of both parts. Analysis of constructions with parataxis with available dominating syntactical level demonstrated specific features of poetical structure of texts of four analyzed authors Vinhranovskyi, Drach, Moisiienko and Stus. Project follow-up is viewed in the study of correlation between syntactical and semantic analysis of text structures, since the form of poetical text specifies of how the poetical language is shaped in sense of qualitative features. Study of construction with parataxis within both a single author’s style and an array of other authors’ styles presents interest and future prospects from the standpoint of carrying qualitative semantic analysis of parataxis with available homogenous items. It is possible since all variants of lexical meaning in the Corpora (their number reaches over 120 K) are automatically marked with special codes of taxonomic classification and with code showing relation to some or other taxon. This technology makes possible to conduct a comprehensive analysis of constructions with parataxis, to create lists and sort out relations of such type of constructions within the range of a dependency tree to detect and identify common and miscellaneous features in the structure of parataxis as a parameter of an individual writing style of a single author.
Understanding the model representation of clouds based on visible and infrared satellite observations

Abstract. Satellite observations provide a wealth of information on atmospheric clouds and cover almost every region of the globe with high spatial resolution. The measured radiances constitute a valuable data set for evaluating and improving clouds and radiation representation in climate and numerical weather prediction (NWP) models. An accurate, bias-free representation of clouds and radiation is crucial for data assimilation and the increasingly important solar photovoltaic (PV) power production prediction. The present study demonstrates that visible (VIS) and infrared (IR) Meteosat SEVIRI observations contain valuable and complementary cloud information for these purposes. We analyse systematic deviations between satellite observations and convection-permitting, semi-free ICON-D2 hindcast simulations for a 30-day period with strong convection. Both visible and infrared satellite observations reveal significant deviations between the observations and model equivalents. The combination of infrared brightness temperature and visible solar reflectance allowed to attribute individual deviations to specific model shortcomings. Furthermore, we investigate the sensitivity of model-derived VIS and IR observation equivalents to modified model and visible forward operator settings to identify dominant error sources. The results reveal that model assumptions on subgrid-scale water clouds are the primary source of systematic deviations in the visible spectrum. Visible observations are, therefore, well-suited to advance this essential model assumption. The visible forward operator uncertainty is lower than uncertainties introduced by model parameter assumptions by one order of magnitude. In contrast, infrared satellite observations are very sensitive to ice cloud model assumptions. Finally, we show a strong negative correlation between VIS solar reflectance and global horizontal irradiance. This implies that improvements in VIS satellite reflectance prediction will coincide with improvements in the prediction of surface irradiation and PV power production.
Co-skewness, co-kurtosis and their implications on asset pricing of cryptocurrencies

This study examines several asset pricing specifications applied for a sample of 72 cryptocurrencies. We extend the existing literature on asset pricing of cryptocurrencies by including higher co-moment factors, namely co-skewness and co-kurtosis. Our overall conclusion is that co-skewness and co-kurtosis are also priced in crypto-markets, but less pronouncedly than in equity/commodity/derivatives markets. Size and momentum factors further increase explanatory power, but their regression coefficients are insignificant.
Sustaining the Effectiveness of IoT-Driven Intrusion Detection over Time: Defeating Concept and Data Drifts

&lt;p&gt;This paper addresses the challenge of sustaining the intrusion detection effectiveness of machine learning-based intrusion detection systems in the Internet of Things (IoT) in the presence of concept and data drifts. Data drift is a phenomenon which embodies the change that happens in the relationships among the independent features, which is mainly due to changes in the data quality over time. Concept drift is a phenomenon which depicts the change in the relationships between input and output data in the machine learning model over time. To address data drifts, we first propose a series of data preparation steps that help improve the quality of the data and avoid inconsistencies. To counter concept drifts, we capitalize on an online deep neural network model that relies on an ensemble of varying depth neural networks that cooperate and compete together to enable the model to steadily learn and adapt as new data come, thus allowing for stable and long-lasting learning. Experiments conducted on a real-world IoT-based intrusion detection dataset, designed to address concept and data drifts, suggest that our solution stabilizes the performance of the intrusion detection on both the training and testing data compared to the static deep neural network model, which is widely used for intrusion detection.&lt;/p&gt;
Colloidal Gold Adsorption on Phyllosilicates and the Correlation of Gold and δ<sup>7</sup>Li: Insights from Kirkland Lake, Hemlo, Hardrock and Red Lake World-Class Gold Deposits, Canada

Alkali metasomatism is common in most, if not all hydrothermal ore deposits, as the mobile alkali elements Na and K may be either remobilized from precursor rocks or precipitated as new minerals.Lithium is also an alkali element, but its presence in trace concentrations normally bounds it to non-Henrian partitionning behavior.Apart from major Li-hosting minerals (e.g., spodumene, lepidolite), Li is commonly hosted in phyllosilicates (micas and clay minerals) and its concentration in these minerals should in principle reflect that in the related fluids.Furthermore, Li possesses 2 stable isotopes that can be ratioed in rocks and minerals: the Li isotopic ratio (δ 7 Li) of hydrothermally-altered ores may hence provide information about mineralizing fluids.Drill core samples from Kirkland Lake Inc.'s Macassa gold mine, one of the world's richest Au deposit, and surface and underground samples from Barrick's Hemlo gold deposit, one of Canada's greatest gold camp, both show good correlations between δ 7 Li and Au in the hydrothermal halo surrounding the deposits.Laser ablation ICP-MS maps of hydrothermal alterations from Newmont's Red Lake mine, Canada's 3rd greater gold camp, were constructed to verify which mineral(s) hosted the Li and where was the Au hosted.Gold was found in trace concentrations (0.4-10 ppm) in sericite (an illite-muscovite-type white mica), in concentrations greater than in adjacent sulfides.Finally, a variety of indirect methods were employed on drill cores from Greenstone Gold Mine's Hardrock gold deposit, one of the most promising gold project in Canada, to verify if Au had adsorbed to various phyllosilicates.It was concluded that colloidal Au was transported downstream the hydrologic flow and had subsequently adsorbed to chlorite.These findings demonstrate that as a very mobile and fast-diffusing element, Li may follow gold not only during its hydrothermal emplacement, but also during subsequent weathering and supergene enrichments.Given that fluid-rock reactions are a major control on Li isotope fractionation, δ 7 Li appears to be a very efficient tool to study the genesis of-, and explore for gold deposits.
Internet marketing

Retrofit and design of thermal cladding systems provide an opportunity to improve the acoustic properties of a building.However, the complexity of the calculation process to predict sound insulation improvement may inhibit rather than encourage novelty and innovation.This paper investigates whether it is realistic to calculate the frequency dependant sound insulation improvement due to modern thermal insulation wall lining systems with just a few input parameters.The calculation procedure is tested using measured results for one external thermal insulation composite system (ETICS) and three curtain wall systems.The accuracy of the procedure is examined using three factors: (1) precision of the measurement, (2) variation of some of the basic parameters of the calculation procedure, and (3) an estimation of the standard error of the calculation.For the ETICS, agreement within <6.0 dB is achieved across much of the frequency range and the trend of the extended dip due to the spring-mass action of the panel is corroborated.The case for using this methodology on curtain wall systems is adequate, however, the trend of calculated results is mostly outside of the 95% confidence limits of the measured results.Possible reasons for this include lack of airtightness of all curtain wall systems and additional transmission due to radiation into and out of the cavity, neither of which are included in the model.The assumption of radiating points or lines, rather than a radiating surface involving the whole panel, gave better agreement at high frequencies for three of the four measured systems (f ≥ 2500 Hz).
Examining the Potential of Blockchain Technology to Meet the Needs of 21st-Century Japanese Health Care: Viewpoint on Use Cases and Policy (Preprint)

<sec> <title>UNSTRUCTURED</title> Japan is undergoing a major population health transition as its society ages, and it continues to experience low birth rates. An aging Japan will bring new challenges to its public health system, highlighted as a model for universal health coverage (UHC) around the world. Specific challenges Japan’s health care system will face include an increase in national public health expenditures, higher demand for health care services, acute need for elder and long-term care, shortage of health care workers, and disparities between health care access in rural versus urban areas. Blockchain technology has the potential to address some of these challenges, but only if a health blockchain is conceptualized, designed, localized, and deployed in a way that is compatible with Japan’s centralized UHC-centric public health system. Blockchain solutions must also be adaptive to opportunities and barriers unique to Japan’s national health and innovation policy, including its regulatory sandbox system, while also seeking to learn from blockchain adoption in the private sector and in other countries. This viewpoint outlines the major opportunities and potential challenges to blockchain adoption for the future of Japan’s health care. </sec>
A study on Teaching Method of Control Engineering by Using Python Based PID

PID controller is so popular control engineering and it has been using in the industrial fields widely.So, it is important to have a lecture for design and theory.This paper deals with design and lecture skill through implementation of PID controller by using Python.So far, many universities and research centres have been using MATLAB to teach and learn in control engineering.However, MATLAB is commercial S/W and expensive.Therefore, developing country's universities have a very difficult because of no license.Currently, many are interesting in Python because it is open source and huge communities.It is a good tool to teach and learn as teacher and students.This paper provides on how we can teach and learn control engineering through implementation of PID controller.
Using Artificial Intelligence Algorithms for Speech Therapy Systems

This article analyzes the algorithms used to process speech signals. A structure for processing Uzbek speech signals has been proposed. Based on this structure, the effectiveness of recognizing Uzbek speech sounds, syllables and words was evaluated on the basis of intellectual algorithms. Based on the evaluated indicators, the structure of neural networks that recognize children's speech pronunciation was developed. Recognition indicators of children’s speech sounds, syllables, and words were identified.
Memory Based Hybrid Dragonfly Optimization for Multiple Key Generation using Cloud Computing

Cloud Computing, as a new technology with enormous computing services, evolved in recent era. It is a most promising computing that gives services on - demand. The Cloud services becomes a noticeable paradigm, by it notable features like, shared pool of resources, Shared infrastructure, dynamic provisioning, network access, handled assessing forward with gassed-up, gullibility, resilience and adoptable. Likewise, it has influence, Cloud Computing has some issues like security of data transferred via the Cloud, availability of resources and its authenticity, remains as a major task of attention. A Novel Optimized Encrytion-As-A-Service is presented in this paper, with multiple keys generation methodology. The three various Key generation includes, Pseudo Random Number Generator (PRNG), Sub-optimal keys are generated from hybridization of Improved Cipher Block Chaining (ICBC) encryption algorithm and final key is from Memory based Hybrid Dragonfly Optimization Algorithm (MHDA). In turn, MHDA is the combination of Dragonfly Optimization Algorithm (DA) and Particle Swarm Optimization Algorithm (PSO) for generating the innovative key for encryption of data. MHDA gives the better performance analysis compared with DA and PSO optimization approaches. The milestone of this optimized hybridization algorithm is to reduce the time complexity and increase the quality of encryption of the data. The experimental analysis is done for Text, image data and performance metrics are evaluated for the proposed research work. Different parameter that explores the main capacity and strength of the algorithm is examined.
Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies (Preprint)

<sec> <title>BACKGROUND</title> Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing. </sec> <sec> <title>OBJECTIVE</title> This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution. </sec> <sec> <title>METHODS</title> The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA’s distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification. </sec> <sec> <title>RESULTS</title> A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing. </sec> <sec> <title>CONCLUSIONS</title> DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices. </sec>
Sequential Models

So why do we need a recurrent neural network (RNN)? Let’s try to answer that with an example, or analogy. When reading a new article, people have two options. First, if they can’t understand it, they can read articles that the new article is based on, for background information. Otherwise, they do understand the new article, based on some prior knowledge of the subject, without an immediate need to read similar articles. In both cases, their ability to understand the new article is enabled by some preexisting or preaquired knowledge. But they don't need to go back to the phase of learning alphabets or numbers; they only need to know what this article is about. This is the way recurrent neural networks work.
Emvitet- project developing education 4.0 competencies - impacts and experiences during covid-19

The aim of this article is to describe the direct and potential impacts Erasmus+ Capacity Building Higher Education (CBHE) - Project called “Empowering Vietnamese VET teachers for transformation towards Education 4.0” (EMVITET)- after one year of implementation. Additionally, the survey findings also summarized the institutional and teachers’ personal experiences during the Covid-19 pandemic, which pushed Vietnamese institutions to rapidly transform their education to online environments. Qualitative data were collected with online questionnaires from six partner institutions and 35 teacher participants. The findings indicate that both individual teacher participants and partner institutions were given a boost to improve Education 4.0 competencies thanks to the participatory and collaborative approach of the EMVITET- project. The project was perceived as an useful external source for supporting the institutions’ management in organizing demanding online education during Covid-19 pandemic.
Analysis of Community Sentiments Regarding Plans to Relocate National Capital Using the Naïve Bayes Method

This study aims to analyze sentiment towards the transfer of new capitals derived from comments on the tweeter. The method used in this research is Naïve Bayes Classifier, a classic method that has a pretty good accuracy. Naive Bayes Classifier is a probabilistic classification based on the Bayes theorem, taking into account naïv independence assumptions. In addition to using the naïve bayes method, in this study the researchers also used word weighting. The weighting word used is TF-IDF, which is a combination of term frequency and inverse document frequency. By using 3 testing methods, namely Confusion matrix, Precission and Recall, and K-Fold Cross Validation. The results obtained in this study are 3 document classifications, namely Positive, Negative and Neutral. Testing is done by dividing the document into 2 subsets, namely training data and test data and the resulting accuracy of 64.6%.