Reviewerly/exHarmony huggingface dataset:
Analisis Kesalahan Berbahasa Bidang Morfologi dalam Laporan Isu Hoaks Corona Virus oleh KOMINFO

This research aims to describe the language errors in the field of morphology, especially in the affixation process and its justification in the Issue Hoax Corona Virus Report by KOMINFO. The benefit of research is to provide insight and knowledge for readers on the use of good and correct Indonesian in the context of writing. Affixation errors cannot be ignored, because at first glance the error is not visible, but it is very important considering that many readers are interested in explaining the reasons why the corona virus or covid-19 hoax issues. This approach uses descriptive qualitative research methods with data collection techniques refer to, record and take samples. The method used to analyze the data is the aggregate method. The results of this study indicate that there are still many language errors even repeatedly in the report on the issue of corona virus hoaks by KOMINFO. Errors in this study include 8 prefix errors, 0 infix errors, 18 suffix errors and 12 confix errors.This research aims to describe the language errors in the field of morphology, especially in the affixation process and its justification in the Issue Hoax Corona Virus Report by KOMINFO. This approach uses descriptive qualitative research methods with data collection techniques refer to, record and take samples. The method used to analyze the data is the aggregate method. The results of this study indicate that there are still many language errors even repeatedly in the report on the issue of corona virus hoaks by KOMINFO. Errors in this study include 8 prefix errors, 0 infix errors, 18 suffix errors and 12 confix errors.
GlossReader at SemEval-2021 Task 2: Reading Definitions Improves Contextualized Word Embeddings

Consulting a dictionary or a glossary is a familiar way for many humans to figure out what does a word in a particular context mean.We hypothesize that a system that can select a proper definition for a particular word occurrence can also naturally solve tasks related to word senses.To verify this hypothesis we developed a solution for the Multilingual and Cross-lingual Word-in-Context (MCL-WiC) task, that does not use any of the shared task data or other WiC data for training.Instead, it is trained to embed word definitions from English WordNet and word occurrences in English texts into the same vector space following an approach previously proposed by Blevins and Zettlemoyer (2020) for Word Sense Disambiguation (WSD).To estimate the similarity in meaning of two word occurrences, we compared different metrics in this shared vector space and found that L1-distance between normalized contextualized word embeddings outperforms traditionally employed cosine similarity and several other metrics.To solve the task for languages other than English, we rely on zero-shot cross-lingual transfer capabilities of the multilingual XLM-R masked language model.Despite not using MCL-WiC training data, in the shared task our approach achieves an accuracy of 89.5% on the English test set, which is only 4% less than the best system.In the multilingual subtask zeroshot cross-lingual transfer shows competitive results, that are within 2% from the best systems for Russian, French, and Arabic.In the cross-lingual subtask are within 2-4% from the best systems.
Vision-based transverse vibration displacement measurement of hoisting vertical ropes in mine hoists

At present, there is no appropriate way to measure the transverse vibration response of moving hoisting vertical rope in hoist. Therefore, a vision-based measurement method combining the digital image correlation (DIC) and digital image processing (DIP) algorithms is proposed in this paper. In this method, a reference line perpendicular to the vertical ropes is added in image sequence by DIP algorithm to form some virtual cross targets, which makes the improved DIC algorithm with adaptive template updating (ATU) rule can track the moving hoisting vertical rope without any labels. Then for distinguishing all ropes in the measuring area, a displacement threshold is set to locate the current measured rope and exclude the other ropes. The transverse vibration displacements of the hoisting vertical rope in an actual mine hoist was measured in three background situations, verifying the feasibility of the proposed method. Moreover, in a laboratory artificial vibration test, the measurement results from the proposed vision method and a laser displacement sensor yielded a very good agreement. The two experimental results indicate that it is fairly reasonable and effective to measure the transverse vibration displacements of hoisting vertical ropes.
Trade Secrets, Big Data, and the Future of Public Interest Litigation Over Artificial Intelligence in Canada

To safeguard big data, many commercial entities deploy legal arguments that these data are trade secrets. Using a Canadian legal backdrop, this article suggests that this argument will hardly be robust, sustainable, or convincing when public interest litigation begins targeting issues of bias and discrimination in artificial intelligence. For now, trade secret litigation is an arena of primarily commercial interests. However, this article suggests that the default view among concerned companies that big data are necessarily trade secrets will be susceptible to attack when public interest litigants turn their sights on matters of bias and discrimination.
A Particle PHD Filter for Dynamic Grid Map Building towards Indoor Environment

The PHD (Probability Hypothesis Density) filter is a sub-optimal multi-target Bayesian filter based on a random finite set, which is widely used in the tracking and estimation of dynamic objects in outdoor environments. Compared with the outdoor environment, the indoor environment space and the shape of dynamic objects are relatively small, which puts forward higher requirements on the estimation accuracy and response speed of the filter. This paper proposes a method for fast and high-precision estimation of the dynamic objects’ velocity for mobile robots in an indoor environment. First, the indoor environment is represented as a dynamic grid map, and the state of dynamic objects is represented by its grid cells state as random finite sets. The estimation of dynamic objects’ speed information is realized by using the measurement-driven particle-based PHD filter. Second, we bound the dynamic grid map to the robot coordinate system and derived the update equation of the state of the particles with the movement of the robot. At the same time, in order to improve the perception accuracy and speed of the filter for dynamic targets, the CS (Current Statistical) motion model is added to the CV (Constant Velocity) motion model, and interactive resampling is performed to achieve the combination of the advantages of the two. Finally, in the Gazebo simulation environment based on ROS (Robot Operating System), the speed estimation and accuracy analysis of the square and cylindrical dynamic objects were carried out respectively when the robot was stationary and in motion. The results show that the proposed method has a great improvement in effect compared with the existing methods.
Hilbert–Schmidt Independence Criterion Subspace Learning on Hybrid Region Covariance Descriptor for Image Classification

The region covariance descriptor (RCD), which is known as a symmetric positive definite (SPD) matrix, is commonly used in image representation. As SPD manifolds have a non-Euclidean geometry, Euclidean machine learning methods are not directly applicable to them. In this work, an improved covariance descriptor called the hybrid region covariance descriptor (HRCD) is proposed. The HRCD incorporates the mean feature information into the RCD to improve the latter’s discriminative performance. To address the non-Euclidean properties of SPD manifolds, this study also proposes an algorithm called the Hilbert-Schmidt independence criterion subspace learning (HSIC-SL) for SPD manifolds. The HSIC-SL algorithm is aimed at improving classification accuracy. This algorithm is a kernel function that embeds SPD matrices into the reproducing kernel Hilbert space and further maps them to a linear space. To make the mapping consider the correlation between SPD matrices and linear projection, this method introduces global HSIC maximization to the model. The proposed method is compared with existing methods and is proved to be highly accurate and valid by classification experiments on the HRCD and HSIC-SL using the COIL-20, ETH-80, QMUL, face data FERET, and Brodatz datasets.
Energy and cost optimization mechanism for workflow scheduling in the cloud

Cloud computing has become one of the most important platforms for various applications like artificial intelligence, big data, the Internet of Things, and others, especially demand for cloud computing has exploded in recent years. Because of the increased demand for cloud computing, it has become more complex, which can lead to software or hardware failure. Due to the advanced infrastructure, failure may not be detected and repaired at a given timeline and will end up costing higher. In addition, the advent of cloud computing has another advantage over the massive spread of scientific work. Scientific workflow refers to a series of computations that enable data analysis in a distributed and systematic way; because this work flow has a large number of works, energy consumption is a major problem. Besides these issues, it also suffers from system reliability; in the response to these issues, several researchers have designed their mechanism, however they failed to understand cloud complex environment. Therefore, here we have designed a mechanism that efficiently reduces energy consumption, improve the fault tolerance to achieve reliability, performs operations in very less time, and optimize the cost in the workflow model. We have also demonstrated efficient energy optimization techniques by reducing task loads.
Immersive Brain Puzzle: a virtual reality application aimed at the rehabilitation of post-stroke patients

Stroke is the disease that most causes disability in the world due to the many recurrences that can affect the individuals. In this context, this study addresses the proposal and implementation of the Immersive Brain Puzzle that aims to assist the rehabilitation of post-stroke patients and stimulate motor and cognitive activities (dual task) by presenting a virtual reality puzzle. It is assumed that treatment in the first hours after a stroke event can significantly contribute to the patient's progress and recovery; thus, our application was developed to be used in the intensive care unit (ICU). The next stage of this research consists of its validation within the hospital environment to be carried out at the Brazilian Institute of Neuroscience and Neurotechnology (CEPID BRAINN) - Unicamp.
An Artificial Intelligence Approach for Word Semantic Similarity Measure of Hindi Language

AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses.Conversational Agents are the most promising technique that assists computer users through their operation.An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages.AI is making it possible to transfer this learning.There is a dire need to develop systems that understand secular languages.One such difficult language is Hindi, which is the fourth most spoken language in the world.Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents.Most of the research is concentrated on English and other European languages.This paper presents a Corpus-based word semantic similarity measure for Hindi.An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings.A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures.The method can be adapted in various applications of word semantic similarity or module for any other language.
Using "Three-dimensional Space Resources" for Fenlong Technology to Improve Grain Ecology and Expand Human Living Spaces

Technology has been applied to increase yield by 20%-50%, improve quality by 5%, and retain water by 100% in 40 kinds of crop land and saline-alkali land in 26 provinces of China. This paper clarified for the first time the scientific theory system of Technology using space Fenlong Agricultural Nature Theory and the development of the relative limits of agricultural growth, which provides a huge power support and natural force for expanding human living spaces. Through inventing and creating a scientific and technological system of farming tools, farming machinery, farming modes, and magic weapons for cultivation, Technology can increase grain, promote ecological development, and greatly expand the living spaces of the Chinese nation and achieve sustainable development. Using Technology, China has expanded from the current single cultivated land agriculture to the big pattern agriculture of cultivated land + saline land + degraded grassland + marginal land + desertified land + river water, flexibly used 147 million ha of three-dimensional space resources of land, and the newly increased food, meat, and fish can feed 300 million to 400 million people, increased the water storage by 100 billion m3, and reduced the collection of groundwater by 20 million to 60 billion m3.
End-to-end mBERT based Seq2seq Enhanced Dependency Parser with Linguistic Typology knowledge

We describe the NUIG solution for IWPT 2021 Shared Task of Enhanced Dependency (ED) parsing in multiple languages.For this shared task, we propose and evaluate an End-to-end Seq2seq mBERT-based ED parser which predicts the ED-parse tree of a given input sentence as a relative head-position tag-sequence.Our proposed model is a multitasking neuralnetwork which performs five key tasks simultaneously namely UPOS-tagging, UFeattagging, Lemmatization, Dependency-parsing and ED-parsing.Furthermore we utilise the linguistic typology available in the WALS database to improve the ability of our proposed end-to-end parser to transfer across languages.Results show that our proposed Seq2seq EDparser performs on par with state-of-the-art ED-parser despite having a much simpler design.
Scaling the Phish: Advancing the NIST Phish Scale

Organizations use phishing training exercises to help employees defend against the phishing threats that get through automatic email filters, reducing potential compromise of information security and privacy for both the individual and their organization. These exercises use fake and realistic phishing emails to test employees’ ability to detect the phish, resulting in click rates which the organization can then use to address and inform their cybersecurity training programs. However, click rates alone are unable to provide a holistic picture of why employees do or do not fall for phish emails. To this end, the National Institute of Standards and Technology (NIST) created the Phish Scale methodology for determining how difficult a phishing email is to detect [1]. Recent research on the Phish Scale has focused on improving the robustness of the method. This paper presents initial results of the ongoing developments of the Phish Scale, including work towards the repeatability and validity of the Phish Scale using operational phishing training exercise data. Also highlighted are the ongoing efforts to minimize the ambiguities and subjectivity of the Phish Scale, as well as the design of a study aimed at gauging the usability of the scale via testing with phishing exercise training implementers.
PERANCANGAN SISTEM INFORMASI PENGOLAHAN DATA PEGAWAI DAN PENGGAJIAN DENGAN METODE WATERFALL (STUDI KASUS PADA PT. INAWAN CHEMTEX SUKSES ABADI)

PT. Inawan Chemtex Sukses Abadi employee data is important information that is means of recording attendance and payroll processes must be managed appropriately. At this time the procedure applied to employee data processing and payroll at PT. Inawan Chemtex Sukses Abadi still uses conventional methods. This method requires a long time in preparing attendance reports and salary reports. With this, PT. Inawan Chemtex Sukses Abadi is necessary to change the data processing methods of employees and payroll that they use today to become computerized and automated methods. To resolve these problems, the researchers make an information system for employee data processing and payroll. Information systems that are made to be able to make attendance reports and salary reports quickly as accountability to company leaders and the expected results of this study are to produce information systems that can process employee data and payroll that are effective and efficient.
A Literature Survey on Wildlife Camera Trap Image processing using Machine Learning Techniques

Motion Triggered Wildlife Camera traps are rapidly being used to remotely track animals and help perform different ecological studies across the globe. The system captures animal visuals that enable the forest department of the respective country to keep track of critically endangered species, record their actions, research environmental changes in order to generate methods This piece of equipment is typically deployed within the forest area in large numbers, resulting in millions of recorded images and videos. It normally takes days, if not months, to go through the dataset completely and, identify the captured animals. In this paper, we study some classifiers of the fauna image that use the convolution neural network to process and identify the wildlife captured by these camera traps.
IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection

In this work, we present our approach and findings for SemEval-2021 Task 5 -Toxic Spans Detection.The task's main aim was to identify spans to which a given text's toxicity could be attributed.The task is challenging mainly due to two constraints: the small training dataset and imbalanced class distribution.Our paper investigates two techniques, semi-supervised learning and learning with Self-Adjusting Dice Loss, for tackling these challenges.Our submitted system (ranked ninth on the leader board) consisted of an ensemble of various pre-trained Transformer Language Models trained using either of the above-proposed techniques.
Estimation of Daily Direct Normal Solar Irradiation Using Machine-Learning Methods

The sizing and simulation of all solar systems require the availability of reliable measurements of solar radiation at different time steps. Unfortunately, solar radiation measurements are not readily available for most worldwide locations. For this reason, it is desirable to develop accurate prediction models by developing relationships between available meteorological data and solar irradiation. Artificial Neural Networks (ANN) have been widely used for the estimation of different solar irradiation components. Recently, some machine learning methods have been reported and appear to be very promising. In this paper, we are interested in comparing the performance of ANN and three ensemble methods (Bagging, Boosting and Random Forests) in estimating the daily direct normal (DNI) solar irradiation from some commonly measured meteorological variables. Our study is performed using measurements data from five Moroccan cities: Oujda, Missour, Erfoud, Zagora, and Tan-Tan. The achieved results show that all developed models give good performances on training and validation datasets with a normalized Root Mean Squared Error (nRMSE) < 20%.
Discretization, the Road to Quantum Computing?

The main challenge we face in making quantum computing a reality is error control. For this reason it is necessary to study whether the hypotheses on which the threshold theorem has been proved capture all the characteristics of quantum errors. The extraordinary difficulties that we find to control quantum errors effectively together with the little progress in this endeavor, compared to the enormous effort deployed by the scientific community and by companies and governments, should make us reflect on the road map to quantum computing. In this work we analyze error control in quantum computing and suggest that discrete quantum computing models should be explored. In this sense, we present a concrete model but, above all, we propose that Quantum Physics should be taken one step further, in order to allow discretization of the quantum computing model.
Probing Cross-Modal Representations in Multi-Step Relational Reasoning

We investigate the representations learned by vision and language models in tasks that require relational reasoning.Focusing on the problem of assessing the relative size of objects in abstract visual contexts, we analyse both one-step and two-step reasoning.For the latter, we construct a new dataset of threeimage scenes and define a task that requires reasoning at the level of the individual images and across images in a scene.We probe the learned model representations using diagnostic classifiers.Our experiments show that pretrained multimodal transformer-based architectures can perform higher-level relational reasoning, and are able to learn representations for novel tasks and data that are very different from what was seen in pretraining.
Methodology of assessing Russian science journals citation in six domains according to Web of Science Core Collection. Part 2

In Part 2, the authors are testing the methods of assessing Russian science journals citation in the natural, agricultural sciences and the humanities based on Web of Science Core Collection. The methodological approach is based on calculating citation coefficient which is a modified version of impact factor, calculated as the ratio of the number of citing documents to the number of scientific articles in Russian journals. The method is tested on the scientific articles published in Russian journals of 2012–2018 and indexed in the Web of Science Core Collection. As a result, recommendations are developed on how to promote Russian journals to the international databases based on Web of Science Core Collection taking into account the specifics of OECD domains under the examination. The general recommendations are: to decrease the share of mono-national publications thus increasing the share of articles written in the international collaboration; and to increase the share of articles by foreign authors. The study is accomplished through the funding within the State Order of the Ministry of Science and Higher Education of the Russian federation “Organizational, technological, research and methodological support of promoting national journals to the international science citation bases and information analytical support of the national subscription to international science information resources for scientific and educational organizations”.
Use of Social Media for Effective Library and Information Services among University Students: A Study

Information communication technology (ICT) plays a key role in the transmission of information between each other. Social networking sites are good examples of a network of communication and it is a good social structural way that allows the user to interact and work with other users. WhatsApp and Facebook are two very popular social media that very much help users to interact each other via text, image, audio, video messaging. This paper was based on a survey that measured the usability of Facebook and WhatsApp for service delivery to the library. An online questionnaire was used to collect information from the respondents. The findings indicated that respondents showed a positive attitude towards the services of library through Facebook and WhatsApp.