Reviewerly/exHarmony huggingface dataset:
Analysis and Determination of Solar Potential Using Models Based on Climatic Parameters for the District of San Mateo, Province of Huarochiri, Lima – Peru

This paper analyzes a procedure to determine the solar potential by means of solar radiation estimation models based on climatic parameters applied at a local level where there are no measurement records. The study was carried out in the district of San Mateo, province of Huarochiri, in the highlands of the Lima region, Peru. Three estimation models were proposed as alternative calculations based on air temperature, in response to the relationship between temperature and radiation and their practical application, including attenuation due to atmospheric effects. The models used were Bristow-Campbell, Hargreaves-Samani and Annandale, of which, the Bristow-Campbell model was selected as it showed better results compared to its peers, obtaining an R2 of 0.818, RMSE of 1.14, MBE of 0.06 as opposed to the R2 of 0.921 and 0.921, RMSE of 1.27 and 1.27, MBE of -0.52 and -0.52 of Hargreaves-Samani and Annandale respectively.To develop the models, a 10-month time series was recorded from the company's own automatic weather station, complementing the information with a 9-year record of historical data extracted from the free NASA application (POWER) for the auxiliary measurement points located throughout the district. The NASA data were validated by correlating them with the data obtained on land, obtaining acceptable results for their application, which allowed generating a data recording grid with the measurement points for performing the simulation in the free software QGIS and obtaining the radiation distribution maps. The average annual radiation was 5.26 kWh/m2day. It is considered that the methodology developed in this research is applicable for estimating the distribution of incident solar radiation at the local level, always considering the conditions of the study area. The global solar radiation values produced in this study can be used in the design and calculation of the performance of solar applications forSan Mateo district.
Generative Landmarks

We propose a general purpose approach to detect landmarks with improvedtemporal consistency, and personalization. Most sparse landmark detectionmethods rely on laborious, manually labelled landmarks, where inconsistency inannotations over a temporal volume leads to sub-optimal landmark learning.Further, high-quality landmarks with personalization is often hard to achieve.We pose landmark detection as an image translation problem. We capture two setsof unpaired marked (with paint) and unmarked videos. We then use a generativeadversarial network and cyclic consistency to predict deformations of landmarktemplates that simulate markers on unmarked images until these images areindistinguishable from ground-truth marked images. Our novel method does notrely on manually labelled priors, is temporally consistent, and image classagnostic -- face, and hand landmarks detection examples are shown.
Regulating What Has Yet To Be Created: An Introduction

The emerging field of synthetic biology—with the potential for engineering life from scratch—has inherited the laws and regulations of its biotechnology precursor. Yet, synthetic biology allows scientists to do entirely new things. This Article considers the resulting legal and ethical issues after surveying the technological capabilities developed within the field of synthetic biology.
Provably Secure Lightweight Key Policy Attribute-Based Encryption for Internet of Things

This chapter presents a lightweight Elliptic Curve Cryptography -based Key-Policy Attribute Based Encryption (KPABE) without bilinear pairing and exponential operations. The proposed scheme is used for payload encryption under MQTT, which provides lightweight security at the application layer of Internet of Things. The chapter provides the necessary background to understand the proposed technique followed by the related work in this area. In context of this, the formal structure of KPABE, MQTT, Access Structure, complexity assumptions and security model are presented as follows. The KPABE scheme generally consists of four algorithms, namely: Setup, Key generation, Encryption and Decryption. The key generation phase of our scheme takes significantly less time as compared to all the other schemes because our scheme employs only scalar addition to generate the keys. The security proof of the KPABE is described by using the security model.
UPB at SemEval-2021 Task 5: Virtual Adversarial Training for Toxic Spans Detection.

The real-world impact of polarization and toxicity in the online sphere marked the end of 2020 and the beginning of this year in a negative way. Semeval-2021, Task 5 - Toxic Spans Detection is based on a novel annotation of a subset of the Jigsaw Unintended Bias dataset and is the first language toxicity detection task dedicated to identifying the toxicity-level spans. For this task, participants had to automatically detect character spans in short comments that render the message as toxic. Our model considers applying Virtual Adversarial Training in a semi-supervised setting during the fine-tuning process of several Transformer-based models (i.e., BERT and RoBERTa), in combination with Conditional Random Fields. Our approach leads to performance improvements and more robust models, enabling us to achieve an F1-score of 65.73% in the official submission and an F1-score of 66.13% after further tuning during post-evaluation.
Serverless GEO Labels for the Semantic Sensor Web

With the increasing amount of sensor data available online, it is becoming more difficult for users to identify useful datasets. Semantic Web technologies can improve such discovery via meaningful ontologies, but the decision of whether a dataset is suitable remains with the users. Users can be aided in this process through the GEO label, which provides a visual summary of the standardised metadata. However, the GEO label is not yet available for the Semantic Sensor Web. This work presents novel rules for deriving the information for the GEO label’s multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. Thereby, this work enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. Further, the prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. Nonetheless, more real-world semantic sensor descriptions, an analysis of requirements for GEO label facets specific to the Semantic Sensor Web, and an integration into large-scale discovery platforms are needed.
Knowledge Management via Human-Centric, Domain-Specific Visual Languages for Data-Intensive Software Systems

We describe the use of a suite of human-centric, domain-specific visual languages to manage knowledge for data-intensive systems. We use two exemplar system case studies – a smart home to support ageing people and a set of smart city technologies – to motivate the need for such an approach. We then describe aspects of these two example systems from abstract requirements to specific data analysis, implementation and deployment choices using our BiDaML representation. We discuss the strengths and limitations of the approach and key directions for further work in this area.
The Art of Reproducible Machine Learning: A Survey of Methodology in Word Vector Experiments

Since the seminal work of Mikolov et al. (2013), word vectors of log-bilinear SVMs have found their way into many NLP applications as an unsupervised measure of word relatedness. Due to the rapid pace of research and the publish-or-perish mantra of academic publishing, word vector experiments contain undisclosed parameters, which make them difficult to reproduce. In our work, we introduce the experiments and their parameters, compare the published experimental results with our own, and suggest default parameter settings and ways to make previous and future experiments easier to reproduce. We show that the lack of variable control can cause up to 24% difference in accuracy on the word analogy tasks.
Dual Language Sentiment Analysis Model for YouTube Videos Ranking Based on Machine Learning Techniques

YouTube is the biggest platform for social media video viewing, sharing web site about 500mints video uploaded on the YouTube in one second. In this huge data when the user search on the YouTube he can’t find desired results and if he found based on the keyword searched still, he can’t find the qualitative desired video content. Its takes too much time to get the desired video content. In Asia the two languages are mostly people use for communication 1. English, 2. Roman Urdu Our proposed works is on the dual language sentiment analysis of the of the YouTube video. Our proposed model helps the user to   rank the video based on the English as well as Roman Urdu. We build a model this will help us to get the review in English and Roman Urdu this will combine them the score and rank the video based on English and Roman Urdu sentiment analysis. We deploy the different machine learning algorithms we find the best classifier for dual language sentiment analysis is logistic regression with count vectorization feature extraction with 87% model accuracy.
RESEARCH OF DIGITAL QUBITES FOR HETEROGENEOUS DIGITAL QUANTUM COPROCESSORS

Recently, interest is growing towards real quantum computers, which are analog and probabilistic devices by nature. Theinterest is also growing to their digital version, both software and hardware. One approach to the construction of real quantum computers is to use quantum chips. The hardware implementation of digital quantum computers involves the use offield programmable gate arrays.A digital quantum coprocessor has already been createdwhich has over a thousand digital qubitsand can perform such complex algorithms as a quantum Fourier transformation. The created and working digital quantum coprocessorcan already be used to work out various quantum algorithms, algorithms for the interaction of a classic computer and its quantum coprocessor, as well as for research various options for building digital qubits.The purpose of this work is to study the effect of the accuracy of the presentation of the state of digital qubit on the probability of obtaining the correct results of the digitalquantum coprocessor.For the study, a heterogeneous digital quantum coprocessor with thirty two digital qubits is selected, which will perform the Fourier quantum transformation. The article describes the basics of building digital quantum coprocessors.Schemes that illustrate the interaction of a classic computer and a quantum coprocessor, the architecture of the coprocessor and the possible structures of its digital qubitsare given. Two variants of the coprocessor, homogeneous one with one pseudo-random codes generator and one comparator, and heterogeneous one, with a generator and a comparator in each digital quantum cell, from which digital qubits consist, are shown.Two options for comparators are also shown -with a direct functional converter and with reverse one.In this work, the influence of the length of the qubit state codes of heterogeneous digital quantum coprocessors on the probability of the correct results formation is investigated.It was shown that the probability of obtaining the correct results at the output of the digital heterogeneous coprocessor is sharply (up to fifty percent) improved with a decrease of the qubit state code length, that is, with a decrease in the coprocessor hardware cost.With a length of a code equal to twobits, the quality of the operation of the heterogeneous coprocessor becomes commensurate with the quality of the homogeneous one. The need for additional research in this direction, including with homogeneous coprocessors, is shown.
<scp>Freedom of Expression</scp>: <scp>Another Look at How Much the Public Will Endorse</scp>

Since World War II, U.S. citizens have reported overwhelming agreement that freedom of expression is a basic right. But, like the law on free expression, public opinion shows that citizen rights to free expression are not absolute or unidimensional, but conditional. To better understand the extent of citizen rights to free expression according to the U.S. public, this study examines data from an online national survey (N = 2,600) in which twenty-five types of expression were offered for respondent agreement that “U.S. citizens should have a right to….” According to the respondents, the free expression types to which citizens have the most rights were expressing political opinions, making a political speech, picketing as a union member, and wearing a black armband in protest. The least endorsed rights were lying in the news, lying generally, protesting outside a church funeral service for a veteran, using racist language in a speech, and burning the American flag. Demographic analyses showed agreement with rights to free expression was highest among younger respondents, non-whites and males. Further analysis confirmed that freedom of expression is not unidimensional, with four main dimensions underlying perceptions of the twenty-five types. These dimensions were identified as repugnant expression, historical political expression, un-patriotic expression, and avoiding compelled expression.
A Novel Anonymity Algorithm for Privacy Preservation

Nowadays, data mining techniques play a major role in obtaining useful data from large amounts of data. Released information may include personal or sensitive information. It is necessary to protect this sensitive information from unauthorized access. This is why, privacy protection becomes an important part of data mining. Over the years, various privacy protection strategies have evolved to protect personal information. After all, Anonymization is one of the most important ways to maintain privacy. A variety of common anonymous methods are used to maintain privacy, however these methods have some flaws in maintaining personal privacy. The Framework for Efficient Anonymous Algorithm is therefore proposed here. Initially, the proposed algorithm aims to identify critical and sensitive data characteristics using the Principal Component Analysis based Attribute Selection Algorithm. The algorithm estimates Eigen values and Eigen vectors. Over time, the process of encryption was performed by introducing the Novel Based Anonymity Algorithm (NBA). Finally anonymous information is available that prevents unauthorized access to personal information. To evaluate the performance of the proposed algorithm many factors such as data usage, privacy levels and computer costs are compared to existing systems. From the experimental analysis, the effectiveness of the proposed system proves its superiority compared to other strategies.
Linguistic and Computataional Aspects of Appraisal Annotation :: The Case of Mobile Application Reviews in English and Spanish

Evaluative language is receiving more and more attention both in the Natural Language Processing (NLP) and in the Corpus Linguistics 8CL) communities, since opinions can be a key aspect in another person's decisions. In order to improve the automatic analysis of evaluative language, it is necessary to develop high-quality cross-linguistic annotated corpora to count on sufficient resources for automated processing. For this reason, this chapter describes the annotation of a corpus of reviews from Play Store with Appraisal features, a very influential theoretical framework in the CL community used to categorise linguistic resources used for negotiating emotions, judgements and evaluations in English and Spanish, as well as the main linguistic conclusions that can be drawn form the use of Appraisal in both languages.
University Financial Management Information System Under the Background of Big Data

With the continuous expansion of the enrollment scale of colleges and universities, the rapid expansion of teaching management in colleges and universities has increased the workload and difficulty of college financial management. This research mainly discusses the construction of college financial management information system under the background of big data. Considering the difficulty of signing, which is the most troublesome leader for reimbursements, develop an electronic approval system. Use classification regression, association rules, change differences and other data mining techniques to build a data warehouse, and then process the data through different distribution methods and multi-dimensional analysis models to provide a decision-making basis for the school to formulate the next annual budget. Through the data processing and analysis system of the big data platform, combined with the historical data of universities and other domestic university data, analyze the level of universities and colleges. Through the information management strategy of colleges and universities, the annual budget revenue of the school in 2019 was 2.274 billion yuan, an increase of 107 million yuan over 2018. This research is of key significance for strengthening financial management of universities and building a financial management information platform.
Desa Tehang Teknologi Tepat Guna E-Government Kotim Untuk Kependudukan dan Administrasi Umum Studi Kasus Desa Tehang

Tehang Village is one of the villages located in Parenggean District. The delivery of population data collection information and certificate services provided is still not ineffective and efficient, because the information and certificate services received by the public and also to the general public are still not computerized. This is because when the village community is interested and in general viewing village information, they must come to the tehang village office to view village information. When the people of Tehang Village submitted a certificate, and the data for the population who submitted the certificate was still stored in computer documents and had not been saved to the system. The obstacle that often arises when residents submit another letter is a letter, a letter stored by an infected virus so that it affects the service to the people of Tehang Village. Therefore, the system that will make researchers is an E-government system in Tehang Village which is used to provide certificate services, making certificates, incoming letters, outgoing letters and Village information to the people of Tehang Village which can be accessed by the people of Tehang Village, or general public
PROTOTIPE AUTOMATIC FEEDER DENGAN MONITORING IoT UNTUK PERIKANAN BIOFLOK LELE MASYARAKAT DUKUH PRAYUNAN

The Industrial Revolution 4.0 has brought many changes both positive and negative. The positive aspect is that there has been much use of automation and robots in the industrial world so that production can increase rapidly. While the negative angle, more and more human work is replaced by machines so that it reduces job opportunities. The existence of the industrial revolution 4.0 also brought a gap between the technology literacy group and the technology stutterer group (gaptek). Villagers are a large group from the second class. For this reason, a new business opportunity that can be carried out by the village community with secondary education level is urgently needed. Then the catfish bio-floc fisheries program was chosen. A touch of automation technology and the Internet of Things (IoT) is given to increase productivity and make people literate about the technological development of the industrial revolution era 4.0.
Re Learning Memory Guided Normality for Anomaly Detection

The authors have introduced a novel method for unsupervised anomaly detection that utilises a newly introduced Memory Module in their paper. We validate the authors claim that this helps improve performance by helping the network learn prototypical patterns, and uses the learnt memory to reduce the representation capacity of Convolutional Neural Networks. Further, we validate the efficacy of two losses introduced by the authors, Separateness Loss and Compactness Loss presented to increase the discriminative power of the memory items and the deeply learned features. We test the efficacy with the help of t-SNE plots of the memory items.
E - VOTING SYSTEM

The problem of traditional-voting-system is time-consuming and security. This paper is with the devel- opment of an online voting system that is advance from the traditional voting system. We use an online voting system to promote awareness and make the election more comforta- ble and more secure by using people Aadhaar card that is stored in the government database. People can log in with their id and linked mobile numbers to participate in voting and choose their favorite candidates to cast vote. This will motivate people to participate in an election without going to the election center.
Automatic Text Summarizing System Using Reinforcement Learning Technique

Automatic document summarization, the process of retrieving key information from a source document is a relevant as well as challenging art. Summarization can be done based on different aspects, on which different works have been done. Extractive summarization and abstractive summarization are such two major types of summarization problems. Extractive summarization is a task in which both machine learning and deep learning techniques have been tested aiming at better results. One of the less used type of machine learning technique called reinforcement learning (RL) was previously used mostly for the task of developing video games such as Atari games and this method is later being used for different NLP tasks too. Au- tomatic summarization using reinforcement learning techniques has become one of the major tasks in the modern research area. Beginning with the traditional reinforcement learning techniques, the research has reached to a state where combination of both deep learning as well as reinforcement learning methods got used together. In this work, we propose a novel abstractive text summarizer which is built using both deep neural network and reinforcement learning technique. The generated summaries are evaluated manually and using average semantic similarity score as well. The system is tested on Amazon fine food review dataset and the results show that the generated summaries are meaningful and context dependent in most of the cases.
Aplikasi Pengelolaan Data Tagihan Iuran pada Divisi Penagihan dan Keuangan BPJS Kesehatan Kantor Cabang Utama Palembang

In the billing and finance division of the BPJS Kesehatan Palembang Main Branch Office, there are 5 types of collection via financial contribution collection, namely via E-mail, WA Blast, SMS Blast, Telecollecting, and Kader. However, to manage the staff/employee financial contribution bill data, they still use Microsoft Excel to filter valid and invalid participant e-mails and cellphones. As well as frequent repetition of billing information to participants. This study aims to design and build a web-based application for data management of dues in the billing and finance division of BPJS Kesehatan Palembang Main Branch Office. Application for Dues Bill Management Data billing and finance division is built using the Prototype method. The results of this study are that this application can make it easier for admins to manage billing data and can filter valid and invalid participant e-mails and cellphones so that they can be grouped by E-mail, Wa Blast, SMS Blast, Telecollecting and Cadres so that information does not happen. billing dues to participants. It is hoped that the application will become a data management machine for dues billing in the billing and finance division of the BPJS Kesehatan Palembang Main Branch Office.