Reviewerly/exHarmony huggingface dataset:
Multilingual Knowledge Systems as Linguistic Linked Open Data

Abstract Creation and re-usability of language resources in accordance with Linked Data principles is a valuable asset in the modern data world. We describe the contributions made to extend the Linguistic Linked Open Data (LLOD) stack with a new resource, Coreon MKS, bringing together concept-oriented, language-agnostic terminology management and graph-based knowledge organisation. We dwell on our approach to mirroring of Coreon’s original data structure to RDF and supplying it with a SPARQL endpoint. We integrate MKS into the existing ELG infrastructure, using it as a platform for making the published MKS discoverable and retrievable via a industry-standard interface. While we apply this approach to LLOD-ify Coreon MKS, it can also provide relevant input for standardisation bodies and interoperability communities, acting as a blueprint for similar integration activities.
Lingsoft Solutions as Distributable Containers

Abstract Lingsoft is one of the leading language technology and language service providers in the Nordic countries. In the Lingsoft Solutions as Distributable Containers (LSDISCO) project, we packaged our language technology tools for distribution as containerised services via the European Language Grid (ELG). As a result, Lingsoft’s speech recognition, machine translation, proofing, and morphological analysis was made available to users of the European Language Grid. The services primarily cover Finnish (general and healthcare domain), Swedish (also Finland Swedish), Danish, Norwegian bokmål and nynorsk, and English. The distribution as containerised services is a straightforward way of making our tools available and updated on ELG and we intend to continue to update our service offerings on ELG with new tools and languages as we develop them.
European Language Grid: Introduction

Abstract Europe is a multilingual society with 24 European Union Member State languages and dozens of additional languages including regional and minority languages as well as languages spoken by immigrants, trade partners and tourists. While languages are an essential part of our cultural heritage, language barriers continue to be unbreachable in many situations. The only option to enable and to benefit from multilingualism is through Language Technologies (LTs) including Natural Language Processing (NLP), Natural Language Understanding (NLU) and Speech Technologies. The commercial European LT landscape is dominated by hundreds of SMEs that develop many different kinds of LTs. While the industrial and also the academic European LT community is world-class, it is also massively fragmented. This chapter is an introduction to the present volume, which describes the European Language Grid (ELG) cloud platform, initiative and EU project. The ELG system is targeted to evolve into the primary platform and marketplace for LT in Europe by providing one umbrella platform for the entire European LT community, including research and industry, enabling all stakeholders to showcase, share and distribute their services, tools, products, datasets and other resources. At the time of writing, the ELG platform provides access to more than 13,000 commercial and non-commercial language resources and technologies covering all official EU languages and many national, co-official, regional and minority languages.
European Language Technology Landscape: Communication and Collaborations

Abstract The European Language Technology community is a diverse group of stakeholders that is characterised by severe fragmentation. This chapter provides an overview of the stakeholders that are relevant for the European Language Grid. We also briefly describe our communication channels and strategies with regard to the promotion of ELG. Furthermore, we highlight a few of the current projects and initiatives and their relationship to and relevance for ELG, especially with regard to collaborations. The overall goal of the target group-specific communication strategy we developed is to create more and more uptake of ELG in the European LT community, eventually creating a snowball effect.
Exploring the Mechanism of Self-Attribution Occurrence using Multiple Dummy Cursors

Virtual reality and robotics technologies have developed interactions with augmented and replicated bodies in digital space. By simultaneously moving multiple bodies in various locations in ample space, users can reach distant objects and interact with them by switching the bodies without having to make significant movements themselves. Previous researches has investigated the design interfaces for manipulation and switching in such interactions using multiple bodies based upon the user's perception of the objects. However, the mechanism by which a self-attribution arises from multiple objects reflecting self-motion is still unclear. To investigate this mechanism, we developed a dummy cursor experiment in which participants were presented with multiple independently moving dummy cursors in addition to the cursors they moved on the screen. The presented dummy cursors moved in different directions and were temporally synchronized with the participants' movements. Participants identified the cursor whose movement exactly matched the mouse they were manipulating from among the multiple dummy cursors displayed on the screen. To investigate under what conditions participants could identify the cursor they were operating, we compared the identification rates by varying the number of dummy cursors presented and how they were moved. As a result, participants could identify the cursor they were operating with a very high probability, even when there were many dummy cursors. Through these results, we discuss the occurrence of a self-attribution among multiple objects related to self-motion and the possibility of parameters contributing to self-attribution.
Analysis of Risk Management Digital Library Services using Octave Allegro Framework

A library service that provides information on collections of books and journals that can be accessed online is called the Digital Libraryvia digilib.uad.ac.id.Digital Library allows for risks that can interfere with information assets and organizational goals.This study uses the Octave Allegro framework which aims to analyze risk assessments so that organizations can choose a mitigation approach to risks that may occur and provide recommendations.The data collection process was carried out by means of interviews, filling out worksheets and questionnaires.The results of this study are expected to be able to calculate how many priority threats are based on the greatest possibility and the appropriate mitigation approach, and can provide recommendations on the form of strategies that can be taken to reduce risks according to the approach taken.Risk assessment on the form of strategies related to mitigation measures focuses on three levels of container categories, namely technical containers, people containers, and physical containers on the form of risk priorities.
A Review on Unstructured Data Processing in Hybrid Cloud Platform

Cloud storage systems are widely employed in many applications due to their improvement in cost, storage availability and security. Hybrid cloud platform refers to the architecture of a cloud system that combines more than one computing environments at a time. It can be either with one public and one private platform or the combination of two private or two public platforms. The hybrid cloud platform has the ability to share the information among the connected systems and that can be processed parallelly while accessing the data. The data that are stored in cloud platforms are mostly in unstructured format that could not be used for any applications like prediction, recommendation, and estimations. This paper reviews the attainments of the previous works that were used for data distribution and partitioning in a hybrid cloud platform, by ensuring the privacy and security of the stored data. The work also explores the future directions on the unstructured data processing by summarizing the research issues observed from the review analysis.
Aspect based Nepali Text Sentiment Analysis using BiLSTM

Aspect-Based Sentiment Analysis is a text analysis technique that classifies the sentiment of a specific aspect in a text. With a rise in internet and social media access, people tend to decide to buy a product or to visit a restaurant based on comments and reviews. Categorical reviews can play a significant role in improving the business of a restaurant if a business entity can capture insights of those reviews and use productively. Since Nepali is a low resource language there is no significant numbers of the dataset for restaurant reviews. Therefore a dataset is created in the work to model a target aspect-based sentiment analysis in restaurant review domain. The dataset comprises of comments extracted from social media sites, websites, and manually prepared data. Data are cleaned, pre-processed then aspects and sentiments are extracted using POS tagging. Sentiments and target categories are classified using word embedding and the deep learning BiLSTM model. The sentiment classification model classifies the sentiments into positive or negative. The target classification model classifies text into 5 target categories Food (F), Price (P), Service (S), Hygiene (H), and Experience (E). The result was found to be good compared to similar models in other domains. The sentiment prediction model and target category prediction model attains the F1 Score of 0.88 and 0.76 respectively.
RANCANG BANGUN APLIKASI E-LEARNING BERBASIS ANDROID DI MIS MUHAMMADIYAH KUBANGKONDANG

The development of Technology and Communication at thus time has a huge impact, one of which is in the field of education, one form of technological development applied in education is a e-learning. Learning activities at MIS Muhammadiyah Kubangkondang are currently carried out face-to-face where the classroom learning activities when teachers of students cannot be present in class and make students feel freedom in carrying out learning become one of the ideas for the creation of an android-based e-learning application design. The research methodology used is the observation and interview data collection method, and the waterfall system development method and UML data modelling. The programming language used kotlin. With this application, the implementation of learning will continue even though the learning time in the classroom has run out because students can view material through the application outside of study hours, when theacers or students are not present in the classroom can carry out learning because teacher can provide material through the application and students can view material through the application.
RANCANG BANGUN APLIKASI PUSAT OLEH OLEH BERBASIS ANDROID DI SATE BANDENG HJ.MARIYAM KOTA SERANG

Toko Sate Bandeng Hj. Mariyam located at Serang currently managed by children of Hj. Mariyam. It is a well-known tourist attraction since 1970. The main dish in the shop is milkfish satay which is always high demand by customers as they still continue to use the original recipe formulated by Hj. Mariyam. The overall sales process still performed manually from taking the order directly on the spot until payment in cash. This process have drawbacks, mainly the reduction of customers due to switching to sellers who already have easier transactions such as online transactions where it can be done anywhere and anytime, as well as problems caused by the incompatibility of the amount of sales with the amount of money in reality. This can cause losses if the calculation is carried out periodically. Another problem faced is the difficulty in the reporting process because it requires a longer time to recap all transaction and still rely on paper. The proposed system design method is waterfalls method, with the modelling use Unified Modelling Language (UML). The proposed system consider mobile-based system using Android. The proposed system can make it easier for consumers to place orders anywhere and anytime and with any payment method and make it easier for owners to manage transactions.
A telepathology based screening tool for COVID-19 by leveraging morphological changes related to leukocytes in peripheral blood smears.

As we approach the aftermath of a global pandemic caused by Severe Acute Respiratory Syndrome-Corona Virus (SARS-CoV-2), the importance of quickly developing rapid screening tests has become very clear from the point of view of containment and also saving lives. Here, we present an explorative study to develop a telepathology-based screening tool using peripheral blood smears (PBS) to identify Coronavirus Disease (COVID-19)-positive cases from a group of 138 patients with flu-like symptoms, consisting of 82 positive and 56 negative samples. Stained blood smear slides were imaged using an automated slide scanner (AI 100) and the images uploaded to the cloud were analyzed by a pathologist to generate semi-quantitative leukocyte morphology-related data. These telepathology data were compared with the data generated from manual microscopy of the same set of smear slides and also the same pathologist. Besides good correlation between the data from telepathology and manual microscopy, we were able to achieve a sensitivity and specificity of 0.83 and 0.71, respectively, for identifying positive and negative COVID-19 cases using a six-parameter combination associated with leukocyte morphology. The morphological features included plasmacytoid cells, neutrophil dysplastic promyelocyte, neutrophil blast-like cells, apoptotic cells, smudged neutrophil, and neutrophil-to-immature granulocyte ratio. Although Polymerase Chain Reaction (PCR) and antibody tests have a superior performance, the PBS-based telepathology tool presented here has the potential to be an interim screening tool in resource-limited settings in underdeveloped and developing countries.
FARANE-Q: Fast Parallel and Pipeline Q-Learning Accelerator for Configurable Reinforcement Learning SoC

&lt;p&gt;This paper proposes a FAst paRAllel and pipeliNE Q-learning accelerator (FARANE-Q) for a configurable Reinforcement Learning (RL) algorithm implemented in a System on Chip (SoC). The proposed work offers flexibility, configurability, and scalability while maintaining computation speed and accuracy to overcome the challenges of a dynamic environment and increasing complexity. The proposed method includes a Hardware/Software (HW/SW) design methodology for the SoC architecture to achieve flexibility. We also propose joint optimizations on the algorithm, architecture, and implementation to obtain optimum (high efficiency) performance, specifically in energy and area efficiency. Furthermore, we implemented the proposed design in a real-time Zynq Ultra96-V2 FPGA platform to evaluate the functionality with an actual use case of smart navigation. Experimental results confirm that the proposed accelerator FARANE-Q outperforms state-of-the-art works by achieving a throughput of up to 148.55 MSps. It corresponds to the energy efficiency of 1747.64 MSps/W per agent for 32-bit and 2424.33 MSps/W per agent for 16-bit FARANE-Q. Moreover, the proposed 16-bit FARANE-Q outperforms other related works by an improvement of at least 1.23× in energy efficiency. The designed system also maintains an error accuracy of less than 0.4% with optimized bit precision for more than eight fraction bits. The proposed FARANE-Q also offers a speed up of processing time up to 1795× compared to embedded SW computation executed on ARM Zynq processor and 280× of computation of full software executed on i7 processor. Hence, the proposed work has the potential to be used for smart navigation, robotic control, and predictive maintenance.&lt;/p&gt;
"Certain Level Operators over Temporal Intuitionistic Fuzzy Sets "

Intuitionistic Fuzzy Sets are substantial extensions of fuzzy sets which play a key factor in describing and providing ease of solving higher complexities in engineering and science.However, certain ambiguous situations cannot be addressed by using fuzzy sets and intuitionistic fuzzy sets.The extension of fuzzy set, namely, Temporal Intuitionistic Fuzzy Sets paved the way for implementing methods and techniques for unanswered problems.In this study, we proposed certain level operators Nα(A), N α (A) and Nα,β(A) for temporal intuitionistic fuzzy sets and investigated some of their properties.We gave numerical examples for TIFS and also study the relations between temporal intuitionistic fuzzy sets of certain level.
A comparative study in Cloudera, Azure and AWS

&lt;p&gt;This research will focus on the differences and similarities between the various Hadoop distributions on-premises or in the cloud. We will also focus on cloud services and components and try to compare them. We will choose Cloudera as Hadoop distribution and AWS, Azure as cloud distribution services. Distributed cloud services have opened the door to easy use and integration of extensive IT services without much manual effort, which is possible with the help of open source communities. However, some enterprises do not see cloud services as a trusted or secure option for their data, so cloud service providers (CSP's) are considering hybrid solutions as a trend. In general, we can see that these distributions are converging in recent years, and enterprises tend to choose the public cloud as a deployment model. This convergence makes any comparison a difficult task. In this study, we will attempt to develop a general comparison method for cloud services and Hadoop distributions that can serve as a basis for future comparison studies."}]}This research will focus on the differences and similarities between the various distributions on-premises or in the cloud. We will also focus on cloud services and components and try to compare them. We will choose as distribution and AWS, Azure as cloud distribution services. Distributed cloud services have opened the door to easy use and integration of extensive IT services without much manual effort, which is possible with the help of open source communities. However, some enterprises do not see cloud services as a trusted or secure option for their data, so cloud service providers (CSP's) are considering hybrid solutions as a trend. In general, we can see that these distributions are converging in recent years, and enterprises tend to choose the public cloud as a deployment model. This convergence makes any comparison a difficult task. In this study, we will attempt to develop a general comparison method for cloud services and distributions that can serve as a basis for future comparison studies. &lt;/p&gt;
Pengaruh Penyuluhan menggunakan Media Booklet dan Video Animasi tentang Sayur Buah terhadap Pengetahuan Remaja SMP IT Bina Insani

A balanced intake of nutrients is needed to meet the body's needs. Vegetables and fruits are foods that are rich in vitamins and minerals. The content of nutrients in vegetables and fruit benefits the health, development and growth of a person's body. The population in Karawang Regency is only about 0.96 who consume more than 5 vegetables and fruits per day a week. This is due to lack of knowledge about vegetables and fruits. The purpose of this study was to determine the effect of nutrition counseling using booklets and animated videos on knowledge about vegetables and fruits in junior high school adolescents. This study uses a quasi-experimental design with a pre-test¬ and post-test group design. The subjects of the study were junior high school teenagers aged 12-15 years, totaling 40 people with 2 intervention groups who received education through booklets and animated videos. The results showed that the value of knowledge before and after the intervention with the booklet group before 78.00 increased to 108.50 and for the video animation group before 74.50 it increased to 98.00. The conclusion is that there is an effect of education using booklets and animated videos about vegetables and fruit on the knowledge of junior high school students.
Aleatoric Uncertainty for Errors-in-Variables Models in Deep Regression

Abstract A Bayesian treatment of deep learning allows for the computation of uncertainties associated with the predictions of deep neural networks. We show how the concept of Errors-in-Variables can be used in Bayesian deep regression to also account for the uncertainty associated with the input of the employed neural network. The presented approach thereby exploits a relevant, but generally overlooked, source of uncertainty and yields a decomposition of the predictive uncertainty into an aleatoric and epistemic part that is more complete and, in many cases, more consistent from a statistical perspective. We discuss the approach along various simulated and real examples and observe that using an Errors-in-Variables model leads to an increase in the uncertainty while preserving the prediction performance of models without Errors-in-Variables. For examples with known regression function we observe that this ground truth is substantially better covered by the Errors-in-Variables model, indicating that the presented approach leads to a more reliable uncertainty estimation.
Streaming Language Processing in Manufacturing

Abstract Often underestimated, (semi-)structured textual data sources are an important cornerstone in the manufacturing sector for product and process quality tracking. The ELG pilot project SLAPMAN develops novel methods for industrial text analytics in the form of scalable, reusable, and potentially stateful microservices, which can be easily orchestrated by domain experts in order to define quality anomaly patterns, e. g., by analysing machine states and error logs. The results are fully available as open source and integrated into the IIoT toolbox Apache StreamPipes.
Universal Semantic Annotator

Abstract Explicit semantic knowledge has often been considered a necessary ingredient to enable the development of intelligent systems. However, current stateof- the-art tools for the automatic extraction of such knowledge often require expert understanding of the complex techniques used in lexical and sentence-level semantics and their linguistic theories. To overcome this limitation and lower the barrier to entry, we present the Universal Semantic Annotator (USeA) ELG pilot project, which offers a transparent way to automatically provide high-quality semantic annotations in 100 languages through state-of-the-art models, making it easy to exploit semantic knowledge in real-world applications.
Heterogeneous multi-population competitive algorithm and its application in ASM1 parameter estimation

Activated sludge model 1 (ASM1) is a biokinetic model of the activated sludge wastewater treatment process, which has several uncertain parameters and complex chemical reaction processes. This model is often used in simulations to assess whether the effluent quality of wastewater meets discharge standards. Accurate and rapid estimation of the parameters of ASM1 is a necessary prerequisite for its successful application in industrial practice. However, ASM1-based parameter estimation is difficult to implement in practical operations due to the slow convergence rate and low convergence accuracy of most parameter estimation algorithms as well as the non-uniqueness of parameter estimation. In view of this, a novel metaheuristic optimization algorithm combining Legendre function network and dynamic partitioning strategy, named heterogeneous multi-group competitive algorithm (HMCA), is proposed to overcome the problem of low algorithmic efficiency and poor practicality in parameter estimation. The performance of the algorithm in terms of convergence speed, convergence accuracy and applicability is verified by two sets of test functions and eight state-of-the-art comparison algorithms. The superior performance of the algorithm in parameter estimation is then validated in conjunction with Benchmark Simulation Model no. 1 (BSM1) and four sets of operational data.