Reviewerly/exHarmony huggingface dataset:
Abstract Strategies and Coherence

Normalisation strategies give a categorical interpretation of the notion of contracting homotopy via confluent and terminating rewriting. This approach relates standardisation to coherence results in the context of higher-dimensional rewriting systems. On the other hand, globular 2-Kleene algebras provide a formal setting for reasoning about coherence proofs in abstract rewriting systems. In this setting, we formalise the notion of normalisation strategy and we prove a formal coherence theorem for convergent abstract rewriting systems.
Efficient Accessibility in Cloud Databases of Health Networks with Natural Neighbor Approach for RNN-DBSCAN

Nowadays, a days, lot of research is done on Cloud computing. It conveys that a huge amount of data is available on the Cloud that needs to be extracted efficiently to get the correct information for providing the specific service on cloud based. the Cloud. Clustering is a widely used data mining technique. The target of clustering is to cluster information as indicated by their likeness, so that it can be used in machine learning, pattern reorganization, etc. In Cloud computing, utilizing the clustering process from a heterogeneous network gets the information to discover the appropriate information. Countless clustering methods are accessible for grouping medical care information on cloud, the Cloud, yet it is hard for individuals with little information on information mining to pick a reasonable clustering technique. This paper analyzes Natural Neighbor to Identify the Density of Data Object, RNN-DBSCAN method, DPC-KNN method, A-DPC method, and LP-SNG algorithm. In the RNN-DBSCAN method, the performance depends upon the value of k. This paper proposes a method of efficient accessibility in Cloud databases of health networks with natural neighbor approach for RNN-DBSCAN using the concept of natural neighbor.
P-468 Demographic and occupational factors associated to mental suffering among healthcare workers in Brazil during COVID-19 pandemic

<h3>Introduction</h3> COVID-19 pandemic changed healthcare routines and increased occupational stress among workers. <h3>Objectives</h3> To analyze the factors associated with mental suffering among frontline healthcare workers (HCW). <h3>Methods</h3> National cross-sectional study carried out in May-June 2020 in Brazil. Participants were invited through social media to fill a questionnaire about sociodemographic, occupational, and clinical data. The Swedish Demand-Control-Support Questionnaire was applied to characterize the work psychosocial context. The World Health Organization´ Self-Reporting Questionnaire (SRQ-20) was used to evaluated the mental suffering. A multiple logistic regression was performed to analyze factors associated to the outcome. <h3>Results</h3> Data were obtained from 437 workers; their average age was 38.4 years (sd±10.0). Most of them were from nursing team (65.0%), female (71.0%) and working in public services (70.1%). Job strain was reported from 23.8% and 52.9% indicated low social support. The overall prevalence of mental suffering was 61.6% and associated factors were: : females (odds ratio - OR 1.93; 95% confidence interval - 95% CI 1.22–3.07), age &lt; 40 years (OR 1.64; 95% CI 1.07–2.52), weekly workload ≥ 60 hours (OR 1.87; 95% CI 1.15–3.11), job strain (OR 2.45; 95% CI 1.41–4.40) and low social support at work (OR 3.47; 95% CI 2.26–5.38). <h3>Conclusion</h3> Individual characteristics and occupational aspects are associated to the suffering in this Brazilian sample of healthcare workers. Policies to improve occupational psychosocial conditions are needed in order to reduce negative mental outcomes, in special during COVID-19 pandemic.
GAN-Based Dual Active Learning for Nosocomial Infection Detection

Although monitor devices and information systems are increasingly connected across hospitals, but scenarios such as hospital acquired infections (HAIs) detection where data are extremely scare and imbalanced has remained to be solved. In this paper, an architecture with generative adversarial networks (GANs) coupled with dual active learning (AL) modules was proposed to mitigate the burden of sampling and learning an inaccurate model caused by imbalanced HAIs datasets. Results showed that generating minority class by GANs could ease the problem of imbalanced data and dual AL modules could make the process of training GANs and sampling generated data more efficient and effective. Experiments on two real-world clinical HAIs datasets confirmed the effectiveness of the proposed framework. GAN balanced dataset improved accuray and f1-score by 7.75% and 12.67% compared to imbalanced dataset. The area of active selection accuracy curve above random selection accuracy curve accumulated to 1.13 and 0.52 from 0 to 50 iterations on two datasets. This approach can be applied in regional healthcare environment as a useful method for handling data scarcity and imbalance.
A Corpus Study of Commissive Speech Acts Performed by USA Presidential Candidates

This study aims to investigate the commissive acts produced by the two USA presidential candidates in 2020 campaign. Campaign is selected as the source of media to gather data since the utterances tend to be more complete and not cut by mediator as if in a debate. The researcher employs a corpus analysis to study the commissive acts produced by USA presidential candidates. The researcher built his own corpus by transcribing the data from the speech of the candidates. There are total 9246 words built as the corpora of this study. The data were then calculated by using Antconc 3.5.8 software to look at the occurrences and class of commissive acts appeared. Further, the researcher also provided an in-depth description of the analysis to support the findings of the study. The study found out that there were more commissive acts performed by Donald Trump compared to Joe Bidden with 87 and 77 respectively. The speech of Donald Trump in his campaign also contains more classes of commissive acts compared to Joe Bidden’s campaign which are 4 classes and 3 classes respectively. It is expected that this study contributes to the board of knowledge in pragmatics area, corpus analysis, and specifically to commissive act production. Specifically, this study contributes to the tendency of politicians in performing commissive acts as a sign of commitment to their voters.
Wireless Sensor Acquisition of Human Motion Parameters Based on Blockchain

With the rapid development of intelligent technology, people’s lives have gradually entered the era of information and intelligence. Traditional vision-based and sensor-based human motion detection methods have shortcomings such as high illumination requirements, difficulty in preserving privacy, and poor portability. The wireless sensor detection method based on a wireless network can overcome the shortcomings of these methods, so it has become a hot research direction. Human motion parameters are very important data. If leaked, it may cause unimaginable harm to individuals. Blockchain technology is an emerging technology in the field of information technology. Its decentralized nature, distributed storage, and data is not easy. The modification provides a feasible solution for us to realize the data upload. Combining the advantages of wireless sensing and blockchain technology, this paper proposes a method of wireless sensing collection of human motion parameters based on blockchain, and designs a human motion recognition system. The experiment found that under the experiments given in this article, the traditional accuracy rate of sensor data collection is 71.82%, and the accuracy rate of the wireless sensing collection method of human motion parameters based on the blockchain in this article has reached 95.31%, which verifies the effectiveness and superiority of the system designed in this article.
Classification of Acute Myeloid Leukemia Subtypes M1, M2 and M3 Using K-Nearest Neighbor

Leukemia is a malignant disease caused by the massive and rapid development of white blood cells in the bone marrow.These excessive white blood cells begin to interfere with the body's mechanism rather than fighting infection.Acute Myeloid Leukemia (AML) is one of the four main types of leukemia with eight subtypes, M0 to M7. AML M1, M2, and M3 have similarities, making them more difficult to distinguish from the other types.Furthermore, they are usually identified by calculating the ratio of myeloblast, promyelocyte, and monoblastic.This research aims to apply the k-Nearest Neighbor (k-NN) in classifying these cell types.k-NN is an algorithm used for classification based on a similarity measure.In cases of finding the best number of neighborhoods, trial and error were conducted.The features needed for classification are cell area, perimeter, roundness, nucleus ratio, mean and standard deviation.Four distance metrics such as Euclidean, Manhattan, Minkowski, and Chebyshev were used in this research.The results show that the Euclidean, Manhattan, Chebyshev, and Minkowski distance successfully identified 207 out of 300 objects at K=18, 197 out of 300 objects at K=13, 209 out of 300 correct objects at K=9, and 210 out of 300 objects at K=7.In conclusion, Minkowski was chosen as the best distance metric for KNN in classifying leukemia-forming blood cells.Furthermore, the accuracy, recall, and precision values of KNN with Minkowski distance obtained from 5-fold cross-validation were 80.552%, 44.145%, and 42.592%, respectively.
MVAD-Net: Learning View-Aware and Domain-Invariant Representation for Baggage Re-identification

Baggage re-identification (ReID) is a particular and crucial object ReID task. It aims to only use the baggage image data captured by the camera to complete the cross-camera recognition of baggage, which is of great value to security inspection. Two significant challenges in the baggage ReID task are broad view differences and distinct cross-domain characteristics between probe and gallery images. To overcome these two challenges, we propose MVAD-Net, which aims to learn view-aware and domain-invariant representation for baggage ReID by multi-view attention and domain-invariant learning. The experiment shows that our network has achieved good results and reached an advanced level while consuming minimal extra cost.
Estimation of PV Power Generation by Linear Regression Model Using Voltage and Current Data

PV systems have the disadvantages of large fluctuations in power and of not being controllable due to external factors.In addition, small-scale PV plants rarely receive maintenance after installation.Managers thus need a monitoring system that predicts the power of the PV plant in order to maintain performance and facilitate O&M.Recently, methods using big data to predict PV plant power have been applied.In this paper, power was predicted through learning based on PV plant field data.Furthermore, the error of the estimated power was analyzed through accuracy evaluations, RMSE, and R 2 analysis.As the learning method, linear regression analysis was applied among machine learning models.Existing linear regression models can immediately estimate power by learning irradiation data as input variables and power data as output variables.However, if the PV system malfunctions, the accuracy of the estimated power generation decreases.In this paper, in order to address this problem, power was estimated by learning irradiation data as input variables and voltage and current data as output variables rather than directly estimating the power.As a result, the RMSE of the proposed linear regression equation was 15.9235kw, yielding a better power estimate than the existing method (16.4241kw).
An integrated approach to the formation of mathematical and natural science literacy of middle school students

The purpose of the article is to describe the author’s comprehensive approach to the formation of mathematical and natural science literacy of middle school students by performing complex tasks with biological content using scientific and methodological foundations and relying on his own experience. The article substantiates the relevance of research in this direction and formulates a contradiction that leads to the problem of finding optimal approaches to the formation of mathematical and natural science literacy of schoolchildren as components of functional literacy. The author offers an integrated approach to the formation of the two components of functional literacy in teaching mathematics in primary school, which correlates with the formation of meta-subject results in accordance with the requirements of the Federal State Educational Standard. Complex tasks with biological content are selected as a means of forming mathematical and natural science literacy, methodological requirements for their compilation are described and examples of tasks are demonstrated. According to the results of the study, conclusions are formulated. The prospects of work in the direction of using complex tasks with scientific content for the formation of functional literacy of students of specialized classes are outlined.
Tryna b Kewl: Textual Analytics of Distorted Words among Malaysian Millennials on Twitter

The presence of Malaysian millennials on social media platforms is increasingly gaining attention particularly on Twitter. Language wise, many of them are predominantly using English and Malay in their tweets but with a touch of their own “styles” in various morphological aspects. This trend eventually leads to a rampant use of distorted vocabulary, churning out many non-standard words. This study aims to address the need in classifying the types of morphological distortions of words that are widely used among the Malaysian millennials and identify the reasons behind such trend. A total of 50 active Twitter users from Malaysia aged 18 to 30 years old were randomly chosen for this study. From each user, 20 tweets of longer than 5 words were selected for lexical analysis, giving a sum of 1000 tweets (8443 words in total). Then, interviews were conducted on 30 participants to gauge the factors of using those non-standard words. The findings revealed that the words were largely distorted in terms of its inflections so as to fit some sounds. Also, most distorted words were deliberately coined so that the millennials would appear trendy, while some were merely following the usage without knowing the actual word. This study has shown that the use of distorted words among Malaysian Twitter users did not hinder effective communication.
A Privacy Preserving Upload Model for Crowd Sourced Health Care Monitoring System

In recent years governments become more concern about health care monitoring which cannot be accomplished without enhancing trust on underlying system as various citizens hesitate to upload their sample because of privacy reasons and obviously the governmental decisions are based on the data collected by various PHCs and third party medical agencies. The accuracy and authenticity of this third party owned data is always doubtful. Crowd sourcing(a collaborative framework) make its sound presence in development of large scale health projects Scientist also impressed from crowd sourcing which is a faster and better alternative to traditional methods for predicting and monitoring infectious diseases. However the success of this type of crowd sourcing depends on the trust on underlying system as the user is always looking firm commitment to preserve their privacy and win a promise of not being re-identified later. Here in this work we suggest a privacy protecting framework for upload process which could fulfill user's diverse privacy requirements while guaranteeing the quality of health care data.
Computing Education, Decent Work and Economic Growth in Nigeria

The Sustainable Development Goal (SDG) 8 is to promote decent work and economic growth. The goal puts at the forefront, workers well-being through employment and decent work that respect the dignity of workers. Countries work towards achieving employment and decent work for inclusive poverty-reducing growth target by 2030. Computing education plays a significant role in achieving the target. This study, therefore, examined the impact of computing education and decent work on economic growth in Nigeria, using Enugu state as a case study. 250 respondents were randomly selected using a structured questionnaire. The Ordinary Least Square (OLS) technique was employed in analyzing the data. Computing education is measured by household investment (enrolment) in ICT/computing education courses. Employment opportunities – employed household members-to-household size, remunerative employment, conditions of work and social security are used to measure decent work. Economic growth, on the other hand, is measured by per capita household income. The results of the study showed that computing education had a significant impact on economic growth. Employment opportunities and social security were also found to had a significant impact on economic growth. Conditions of work had a positive and insignificant impact on economic growth, while remunerative employment had a negative and insignificant impact on economic growth. Computing education and employment opportunities were complementary. Also, there was a complementary effect of computing education and remunerative employment, computing education and decent work, and computing education and social security on economic growth. Among the measures of decent work, conditions of work and social security were complimentary, while employment opportunities and remunerative employment, remunerative employment and conditions of work, and remunerative employment and social security were substitutes. Other variables such as the stock of human and physical capital played some diverse roles in enhancing economic growth. We, therefore, recommend the adoption of supportive policies to encourage computing education and decent work as well as physical and human capital development. In particular, policies to encourage computing education should include the incorporation of compulsory ICT/computing education courses in the nations one-year National Youth Service Corp (NYSC) scheme.
P PENGEMBANGAN GAME EDUKASI “PETUALANGAN ALJA” UNTUK MELATIH CONCEPTUAL UNDERSTANDING DAN PROCEDURAL FLUENCY SISWA SMP MATERI ALJABAR

Games are one of the entertainments that are often and in demand by many children, especially those that can be used in electronic media. The educational game media developed in this research is "Petualangan Alja" which includes algebraic material. Addie model is used in research research and development (R&amp;D) and there are 5 stages of development, namely: analysis, design, development, implementation, and evaluation. This educational game has confirmed its validity as a learning medium by materials and media experts with 95% and 95% results obtained. 5 students of grade 7 of SMP Negeri 1 Getasan and SMP Negeri 6 Salatiga were selected as research subjects. The results showed that the percentage of practicality obtained was 94.7% with a very good category. The significance result of the Paired T-Test test was 0.039 which was less than 0.05 with the learner's learning outcomes increasing significantly from the higher posttest average score of the pretest score. The conclusion of this study is that the educational game "Petualangan Alja" is valid, practical, and effective.
Deep Reinforcement Learning based control algorithms: Training and validation using the ROS Framework in CARLA Simulator for Self-Driving applications

This paper presents a Deep Reinforcement Learning (DRL) framework adapted and trained for Autonomous Vehicles (AVs) purposes. To do that, we propose a novel software architecture for training and validating DRL based control algorithms that exploits the concepts of standard communication in robotics using the Robot Operating System (ROS), the Docker approach to provide the system with portability, isolation and flexibility, and CARLA (CAR Learning to Act) as our hyper-realistic open-source simulation platform. First, the algorithm is introduced in the context of Self-Driving and DRL tasks. Second, we highlight the steps to merge the proposed algorithm with ROS, Docker and the CARLA simulator, as well as how the training stage is carried out to generate our own model, specifically designed for the AV paradigm. Finally, regarding our proposed validation architecture, the paper compares the trained model with other state-of-the-art traditional control approaches, demonstrating the full strength of our DL based control algorithm, as a preliminary stage before implementing it in our real-world autonomous electric car.
Interaction-free delayed-choice scenario

The fully quantum description of the two path interference of a single photon in the Mach–Zehnder interferometer is presented. The interaction-free delayed-choice scenario in the Mach–Zehnder interferometer is proposed to test the fully quantum description of the two path interference. Several schemes of the arrangements designed to realize the interaction-free delayed-choice scenario are considered.
THE PROCESS OF THE MODEL FOR THE DEVELOPMENT OF PUBLIC SERVICES IN THE DEPARTMENT OF LIBRARY AND ARCHIVES IN THE CITY OF BATU (CASE STUDY OF RANGING LIBRARY SERVICES)

The Department of Library and Archives of Batu City is an agency that organizes archiving and library functions. Library management in Batu City is carried out by the Library Division. The duties and functions of the Information Office in the implementation are in accordance with good governance management, namely transparency, accountability, and based on consensus. Business activities carried out in the office are not completely recorded in a standard document in the form of a procedure. Through official procedures, the definition and implementation environment of activities can be determined which can be used as a reference for each official activity. Procedures as a guidance document for each activity in the office need to be written in a graphic form that can make it easier for stakeholders to understand activities and make it easier for those implementing the technology management function to understand the needs through the risks that arise from each activity. Miscommunication and decreasing quality of service in the management of libraries, reading gardens and mobile libraries often occur, especially in connection with problems caused by the absence of written references within the office. Based on these reasons, it is necessary to formulate a process model of activities in the office. Documenting a process model that will truly provide higher quality benefits because it is based on clear references, it can prevents miscommunication, prevents security threats, and increases the transparency of processes that have been happening in the office. The compilation model is carried out through the activity of respondent data, which previously carried out the determination through the RACI Chart technique. Data collection was carried out through surveys so that an output was formed in the form of business process abstracts according to each stakeholder in the agency. After validation, the process will be compiled using the BPMN modeling language so that a standard model is obtained from the mobile library.
Blind Recognition of Parameters of Reed Solomon Code from Intercepted Erroneous Codewords

Error correcting codes are designed for reliable transmission of digital information over a noisy channel. Several papers have been published on blind identification of binary FEC codes but papers reported on the identification of non-binary error correcting codes are less. Due to its strong error correction capability, RS (Reed-Solomon) code is being used widely. So technique for blind recognition of RS code is required to analyse intercepted signal as well as for intelligent communication. This paper presents a technique for extraction of parameters of Reed-Solomon code from intercepted demodulated bitstream. The proposed algorithm is very simple and hence it is very practical for hardware implementation. Our approach has been verified using MATLAB simulation.