Reviewerly/exHarmony huggingface dataset:
Improving Sstudents’ Speaking Skill Through Communicative Language Teaching (CLT) Method at The Tenth Grade of Tebuireng 8 Islamic Boarding School (MA) Banten

The objective of this research was to find out the improvement of Students Speaking Skill through Communicative Language Teaching (CLT) Method at The Tenth Grade of Tebuireng 8 Islamic Boarding School. The researcher used quantitative research implementing true experimental design as the research method. There two classes used in this research. The control class and experimental class. Sample of this research is students Tebuireng 8 Islamic Boarding School, 20 students from each class. The researcher found that there was significant improvement of students speaking skill through CLT method, indicated by the value of t-test higher than the value of t-table (4 > 2.024) it means that alternative hypothesis was accepted. From those findings, the result of this research showed that Improving Students Speaking Skill through Communicative Language Teaching (CLT) Method is highly encouraged.
SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts

Determining coreference of concept mentions across multiple documents is fundamental for natural language understanding. Work on cross-document coreference resolution (CDCR) typically considers mentions of events in the news, which do not often involve abstract technical concepts that are prevalent in science and technology. These complex concepts take diverse or ambiguous forms and have many hierarchical levels of granularity (e.g., tasks and subtasks), posing challenges for CDCR. We present a new task of hierarchical CDCR for concepts in scientific papers, with the goal of jointly inferring coreference clusters and hierarchy between them. We create SciCo, an expert-annotated dataset for this task, which is 3X larger than the prominent ECB+ resource. We find that tackling both coreference and hierarchy at once outperforms disjoint models, which we hope will spur development of joint models for SciCo.
Demographic Factor, Adoption of Technology and Competitive Advantage in Nigeria

Purpose: The general objective of the study is to examine factors influencing the adoption of technology and competitive advantage in Nigeria. The specific objective is to ascertain the influence of demographic factors on a firm’s innovativeness. Design/Methodology/Approach: The study adopted a correlational survey approach and questionnaire as instruments for data collection. Primary data were sourced from a sample of 257 people. The population of the study is made up of postgraduate (Postgraduate Diploma, Master’s and Doctorate) students in eight faculties at the Federal University of Technology Owerri, FUTO for the 2018/2019 academic session. A simple random probability sampling method was adopted in the distribution of the questionnaire. Data were analyzed using Pearson Correlation to test the relationship between the variables.Findings/Originality: Findings revealed that demographic factors (educational level, income and internet experience) were found to have a significant positive influence on organizational innovativeness. The authors recommend the following; Federal Ministry of Education is advised to create a technology-enriched curriculum to provide equal learning opportunities to the working citizens to enable them to enrol in schooling. Government should prioritize investment in classroom technology as well as teachers training in tertiary institutions in the country. Previous studies did not investigate the influence of demographic factors on firms competitiveness at the Federal University of Technology. This study contributes to existing knowledge by identifying the research gaps in the literature and recommends areas for further studies.
THE UTILIZATION OF EDUCATIONAL TECHNOLOGY BASED ON ZOOM MEETING AND GOOGLE CLASSROOM IN THE PANDEMIC ERA

This research aims at how Zoom Meeting and Google Classroom media are used as technology media that could be a learning solution in this Covid-19 era. The method used in this study was based on qualitative research based on case studies, where the authors analyzed the collected data to be used as primary data sources in further research. The problem faced by institutions during the pandemic was that there were still most students who did not have adequate communication tools and internet network access. The ineffectiveness of using Zoom Meeting and Google Classroom learning media at the One Roof Middle School in Lontar was caused by the economic level of the parents of students, most of whom rely financially on seafood, so that most of the students found it difficult to provide supporting infrastructure for online learning. In addition, there were still many students who were clueless in the field of information technology (IT), making it difficult for them to operate the two online learning platforms.
Sistem Pendukung Keputusan Pemilihan Bibit Ikan Air Tawar Berbasis Android

The potential bussiness of fishery will be a good profit in 2021 the consume of fish will reach out 19,6 kiloes by the people in the world by the capita for a years.  the cultivation of the fish germ  in the fishpond increases  about 11 percent for a year with rapidly . Because the are so many cases has found where the breeder does not know how to choose the kinds of fishes which suitable in their region with the condition their’s fishes. So the important factor things is how to choose the best fish germ for getting succeed the cultivation of fish germ. One of the methods are approaches which could be applied in that problem it it called the methods system of deciding espouse. In one of the expalation , so the methods system of deciding espouse has been made on the smartphone with android basis with using methods of WP ( Weighted Product) , which can give the information about the kinds of fish germ , and help the breeder in take of deciding the fish germ that will be breeded. The methods of agile process which has been applied in the researches is Extreme programing (XP) . The result of this research , the methods system of deciding espouse which can give the reccomendation for taking decisions in choosing the fish germ.
Deep Learning Model for Breast Cancer Segmentation

Breast cancer is the second most common type of cancer after lung cancer. Early detection using screening mammography is instrumental in improving the breast cancer prognosis. Lesions in the breast are important signs of cancer and it is a challenging task to segment them with precision even with advanced deep learning methods. In this manuscript, we modify the cutting-edge CNN architecture, U-Net developed for biomedical image segmentation. We replace the encoder segment of the U-Net architecture with scalable and efficient Efficientnetb3 network. The model learns the local and global context in the input mammogram images and predicts a precise pixel-wise segmentation map for the lesion. These detected maps can assist the radiologists in reviewing the lesion location and shape in order to differentiate between benign and malignant lesions. We train and evaluate our model on the full mammographic (MG) scan images from the publicly available curated DDSM dataset (CBIS-DDSM). Our model yielded mean Intersection over Union (IoU) of 0.956 and Dice similarity coefficient of 0.968 between the radiologist marked ground truth masks (GTMs) and the segmented lesion maps generated by our model. This study aims to show that utilizing pre-trained encoder backbones in U-Net can result in faster convergence and high precision in lesion detection and segmentation tasks.
Oblivious Median Slope Selection

We study the median slope selection problem in the oblivious RAM model. In this model memory accesses have to be independent of the data processed, i.e., an adversary cannot use observed access patterns to derive additional information about the input. We show how to modify the randomized algorithm of Matousek (1991) to obtain an oblivious version with O(n log^2 n) expected time for n points in R^2. This complexity matches a theoretical upper bound that can be obtained through general oblivious transformation. In addition, results from a proof-of-concept implementation show that our algorithm is also practically efficient.
Rancang Bangun Sistem Informasi Akuntansi Berbasis Web Menggunakan Metode Fifo

Olivine Mebel is a house appliance store that sells furniture such as spring beds, sofas, cabinets, dining tables, and others. All purchase and sale transactions, as well as inventory, are still done manually so that it is prone to errors in making financial reports and stock reports that can cause losses. Based on the existing problems, a web-based accounting information system is created to support the Olivine Furniture Store in running its business. The accounting information system was built using the PHP programming language and MySQL DBMS (Database Management System) with the help of the Laravel framework. The method used in the inventory valuation method is FIFO (First In First Out) because this method is suitable for the sales department where at the time of making a sale, the goods sold are the first goods purchased. With this system, recording transactions become easier so that financial reports can be processed automatically, and managing the number of stock items becomes more practical and also makes it easier for users to manage their business. This system is tested by using Black-Box Testing so that it can find out how user acceptance of the information system developed. After the system is completed, the testing phase will be carried out. The results of the Blackbox Testing test state that the system has been running as it should and can operate properly. Expert test results state that the accounting system has been running in accordance with the accounting flow
Deep models and optimizers for Indian sign language recognition

Deep Learning has attracted the research community&apos;s attention for a long time, and still, new deep models come into the picture very frequently. It is challenging to know and select the best amongst such models available in the literature. Also, selecting optimizers and tuning optimization hyperparameters is a trivial task. Thus, in this paper, we carry out a performance analysis of two pre-trained deep models, four adaptive gradient-based optimizers, and the tuning of hyperparameters associated with them on a static Indian sign language dataset. Experimental results found InceptionResNetV2 and Adam optimizer to have the potential of being used for static sign language recognition using transfer learning technique. Inception-ResNetV2 model highly outperformed the state-of-the-art machine learning approaches and hand-crafted features with an accuracy of 94.42% and 85.65% on numerals and alphabets of Indian sign language, respectively.
Pourquoi les matrices aléatoires expliquent l'apprentissage ? Un argument d'universalité offert par les GANs

Afin de mettre enevidence les aspects d'universalite des grandes matrices aleatoires sur les donnees reelles, nousetudions dans cet article le comportement spectral de la matrice de Gram pour une large classe de vecteurs aleatoires, les dits vecteurs concentres, qui sont plus riches que des vecteurs gaussiens. L'hypothese de concentration est particulierement motivee par le fait que l'on peut generer, grâce aux GANs, des donnees realistes par transformations Lipschitziennes de vecteurs gaussiens. En particulier, en generant des imagesa travers un GAN, nous montrons que le comportement spectral de la matrice de Gram est le meme sur ces donnees que sur un modele de melange gaussien, rendant ainsi l'estimation des performances de classification predictiblea travers la theorie des matrices aleatoires pour des donnees reelles. Abstract-In order to highlight the universality aspects of large random matrices on real data, we study in this paper the spectral behaviour of the Gram matrix for a large class of random vectors, the so-called concentrated vectors, which are richer than Gaussian vectors. The concentration hypothesis is particularly motivated by the fact that realistic data can be generated using GANs by Lipschitzian transformations of Gaussian vectors. Particularly, by generating images across a GAN, we show that the spectral behaviour of the Gram matrix is the same on these data as on a Gaussian mixture model, thereby making the estimation of classification performance predictable through random matrix theory for real data.
Bank Customer Complaints Analysis Using Natural Language Processing and Data Mining

The banking sector has undergone a major revolution with the advent of digital transformation. The entry of Fintech and tech giants such as Google, Amazon, and Facebook have introduced convenient banking that is easy to understand and use. In this competitive environment, banks are realizing the importance of customer service and satisfaction and want to pay close attention to the Voice of Customer to improve the customer experience. By analyzing and getting insights from customer feedback, banks will have better information to make strategic decisions. In their quest to better understand their customers, banks are seeking artificial intelligence (AI) solutions in the form the of sentiment analysis. What is sentiment analysis? In simple words, sentiment analysis is the process of detecting a customer’s reaction to a product, brand, situation or event through texts, posts, reviews, and other digital content. Using sentiment analysis, business leaders can gain deep insight into how their customers think and feel. The analysis can help in tracking customer opinions over a period of time, determine customer segmentation, plan product improvements, prioritize customer service issues, and many more business use cases.
Optimización de trayectorias para un UAV en agricultura de precisión

This document presents the integration and development of an architecture for the optimal navigation of a quadcopter UAV in high-precision agriculture monitoring tasks. This architecture is composed of mathematical models that describe the non-linear effects of UAV flight dynamics and aerodynamics, as well as models to calculate the energy consumption of the battery and the UAV. From these models, we propose an optimization algorithm to define a flight path that minimizes the energy consumption of the UAV.
Probability Distribution on Full Rooted Trees

The recursive and hierarchical structure of full rooted trees is used in various areas such as data compression, image processing, and machine learning. In most of these studies, the full rooted tree is not a random variable. It causes a problem of model selection to avoid overfitting. One method to solve it is to assume a prior distribution on the full rooted trees. It enables us to avoid overfitting based on the Bayes decision theory. For example, by assigning a low prior probability on a complex model, the MAP estimator prevents the overfitting. Further, we can avoid it by averaging all the models weighted by their posteriors. In this paper, we propose a probability distribution on a set of full rooted trees. Its parametric representation is well suited to calculate the properties of our distribution by recursive functions: the mode, the expectation, the posterior distribution, etc. Although some previous studies have proposed such distributions, they are for specific applications. Therefore, we extract the mathematically essential part of them and derive new generalized methods to calculate the expectation, the posterior distribution, etc.
Drawing Towards Sound

This chapter explores the relationship of drawing to sound and musical notation. It provides a historical grounding from earlier innovations of Post-war 20th Century composers through to contemporary compositional procedures and the work of sound artists. It examines key 20th century composers - Boulez, Cage, Fluxus, Cardew etc. and the inherited problems of notational strategies. The chapter is related to an exhibition curated at the University of Greenwich - Drawing towards Sound'.
Secured Authentication With One Time Password on the Cloud

There are different types of conventional authentication types that include challenge/response mechanisms, passwords or even physical smart cards, the most popular of which are passwords. Most people use the same passwords with different accounts, which can also recover other passwords if compromised. Several recent studies have also proposed authentication based on a one-time password. That attempted to solve the shortcomings of all previous methods but also bring new limitations. The OTPs are sent to the user's telephone or email address, but when the contact is intercepted, the OTP is vulnerable to theft. After referring to a lot of research work in this field, we are trying to solve the above-mentioned problem by implementing another layer of security that contains a user-specialized information factor along with the given OTP. This system consists of four protection levels — traditional, secure, responsive and highly sensitive security deployed on the cloud. These four security levels are designed for different cases of use. In most situations, this method overcomes the problem of OTP theft, as the knowledge factor other than the OTP is limited to the user. In this method, we propose a security layer(s) that can be combined with the existing authentication or can even act as an authentication framework on its own.
Africa, from the past to the present: Moving the critical environmental accounting research on Africa forward

This chapter focuses on environmental accounting on Africa, a continent with huge mineral reserves and extractive activities but with widespread pollution, social inequities and environmental degradation. It captures the centrality of natural resources to Africa and how these resources affect social, economic, environmental, political, governance, accountability, social justice and sustainability issues (e.g. climate change, deforestation and afforestation, environmental pollution, artisanal mining, accounting for water, waste management, non-degradable products, biodiversity and extinction of endangered species, erosion, drought, environmental risk, child labour, modern slavery, and social and environmental justice). We reflected on the trends and gaps in accounting research publications and captured the connections between governance, accountability, and social and environmental issues in Africa. Given the prevalence of the resource curse syndrome in Africa, this chapter reflects on and identifies the direction for future social and environmental accounting, and policy-oriented research necessary to address the urgent set of social and environmental issues if Africa is to become environmentally sustainable while meeting its Sustainable Development Goals. This chapter is a must-read for academics and students with interest in social and environmental issues in Africa.
Formation Of Rural Schoolchildren's Readiness For Moral Choice In Sociocultural Practices

The article reveals the essence of the concept "readiness for moral choice," analyzes various views on the problem of this personality quality formation. According to the authors, the formation of schoolchildren's readiness for moral choice is a focused pedagogical activity aimed at enriching knowledge, realizing morality, mastering the skills to analyze and evaluate their actions and those of other people, and make a moral decision. Various types of interactive and collective-creative activities are recommended as one of the ways to solve the problem. In experimental work, the authors turn to three types of sociocultural practices: real-practical, game, organizational and leisure. The authors attach importance to sociocultural practices of local lore, initiated by the school museum. In accordance with the requirements of the time, students are offered activities in various formats: both in the real and digital educational environment. The article presents the diagnostic study results on manifestations of rural schoolchildren's readiness for moral choice at the beginning and completion of experimental work.
Offline RL With Resource Constrained Online Deployment.

Offline reinforcement learning is used to train policies in scenarios wherereal-time access to the environment is expensive or impossible. As a naturalconsequence of these harsh conditions, an agent may lack the resources to fullyobserve the online environment before taking an action. We dub this situationthe resource-constrained setting. This leads to situations where the offlinedataset (available for training) can contain fully processed features (usingpowerful language models, image models, complex sensors, etc.) which are notavailable when actions are actually taken online. This disconnect leads to aninteresting and unexplored problem in offline RL: Is it possible to use arichly processed offline dataset to train a policy which has access to fewerfeatures in the online environment? In this work, we introduce and formalizethis novel resource-constrained problem setting. We highlight the performancegap between policies trained using the full offline dataset and policiestrained using limited features. We address this performance gap with a policytransfer algorithm which first trains a teacher agent using the offline datasetwhere features are fully available, and then transfers this knowledge to astudent agent that only uses the resource-constrained features. To bettercapture the challenge of this setting, we propose a data collection procedure:Resource Constrained-Datasets for RL (RC-D4RL). We evaluate our transferalgorithm on RC-D4RL and the popular D4RL benchmarks and observe consistentimprovement over the baseline (TD3+BC without transfer). The code for theexperiments is available athttps://github.com/JayanthRR/RC-OfflineRL}{github.com/RC-OfflineRL.
Effectiveness of 4MAT Model in Developing the English Spelling Skill of Grade 8 Students

The purpose of this research was to construct an instructional method for developing English spelling skills by using the 4MAT model entitled In the Garden for grade 8 students of Khamkaen Nakorn School. The level of students’ satisfaction was also studied. The sample of this research comprised 30 grade, 8 students, in the first semester of the 2020 academic year at Khamkaen Nakorn School, Khon Kaen Province. While the independent variable was 4MAT model learning, the dependent variables were 1) the effectiveness of English vocabulary spelling skills, 2) The students’ satisfaction with 4MAT model learning. The research instruments consisted of two units of vocabulary activities or games and procedures, a vocabulary post-test, and a satisfaction questionnaire. The data were analyzed by percentage, mean, and standard deviation. The results of this research were l) the effectiveness of the 4MAT model in developing the English spelling skill was 80/73.67, and 2) students’ satisfaction towards the instructional package was at a high level.
Distributed annotation in virtual reality

The main goal of this work was to create a network environment for the Unity Engine project StolperwegeVR, developed by the Text Technology Lab of Goethe University, in which you will be able to annotate one to several documents in a group. For this, basic network utils like seeing other users or moving objects had to be implemented which had to be easy to use and work with in the future.