Reviewerly/exHarmony huggingface dataset:
A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks

In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-specific tasks, the appropriate (pre)training strategies have not yet been rigorously evaluated in such social media customer service settings, especially under multilingual conditions. We address this gap by collecting a multilingual social media corpus containing customer service conversations (865k tweets), comparing various pipelines of pretraining and finetuning approaches, applying them on 5 different end tasks. We show that pretraining a generic multilingual transformer model on our in-domain dataset, before finetuning on specific end tasks, consistently boosts performance, especially in non-English settings.
Spatial mapping of rice crop evapotranspiration in godavari command area

The aim of this paper is determining potential rice crop evapotranspiration (ETc) using satellite data in Godavari irrigation command area of Andhra Pradesh, India. ETc was estimated from reference evapotranspiration (ETo) and crop coefficient (Kc). ETo estimated using agro-meteorological data (measured maximum and minimum air temperatures) according to FAO-24 modified Blaney-Criddle method. The Kc maps were mapped in Arc GIS software using Normalized Difference Vegetation Index (NDVI). Red (R) and Near Infra-Red (NIR) measurements of MODIS image were used to calculate NDVI. Further, the ETc map was obtained by multiplying Kc map with the ETo map. The maximum ETc computed for Rice crop was 6.04 mm/day, whereas the minimum was 2.22 mm/day for the Godavari command area.
EXTRACTING TEXT FROM THE PICTURE BY USING OCR TECHNOLOGY

— Like it, all known, almost all the cell phones now existing in the world almost have all have at least good quality of back camera except to some phone and also, a few years before, technology enhances and technical industry people launched new application by using this camera on which very big documents is achieved by completely traversing this camera on it. Now, it’s a strong effort to make out to upgrade this application. Now, this application can extract the text from the picture. It is a tricky assignment due to creative actions of mobile camera beside by way of hand on shaking, transform in enlightenment suitable to hand overshade, etc. the matter has extra complied through the detail that transcript alphabets which not hold diverse quality which is mainly an input necessity for panoramic icon invention. An outline is intended for some time to filter the configuration of icons depends upon the supermodel designing. This saves memory by extracting text from images by using OCR technology.
Technology and Mathematics as a Cognitive Component

The objective of the study was to analyze the technological impact and cognitive attributes of technology for the construction of meaningful learning of mathematics students at Universities in Ecuador and Colombia. The sample was non-probability, with the participation of 1124 students. A mixed study of sequential explanatory design was applied. In the quantitative phase, correlational method, were applying a digital questionnaire, the data were analyzed through multiple regression and statistical analysis of nested variance to two variables demonstrating the need to implement mathematical software. In the qualitative phase, the mathematical software Wiris and Geogrebra were implemented and results merged by the contrasting method. The conclusions demonstrate the impact of digital connectivity, distinguishing technology as a cognitive component through dynamic knowledge. The method was effective to the development of logical analytic thinking; boosted the activity of the right limbic lobe.
AI Optics: Object recognition and caption generation for Blinds using Deep Learning Methodologies

With the exponential development in the field of artificial intelligence in recent years, many researchers have focused their attention towards the topic of image caption generation. With this topic being that of arduous task and interest people take it as a challenge to perform to excel in the field of AI. Automatic generation of neutral language descriptions or `captions' according to the composition detected in an image, i.e., scene understanding is the main part of image caption generation which can be achieved by combining both natural language processing along with computer vision. In this paper, we tackle the task of generating captions by using the concepts of Deep Learning.
Robust Forecasting through Generalized Synchronization in Reservoir Computing

Reservoir computers (RC) are a form of recurrent neural network (RNN) used for forecasting timeseries data. As with all RNNs, selecting the hyperparameters presents a challenge when training onnew inputs. We present a method based on generalized synchronization (GS) that gives direction in designing and evaluating the architecture and hyperparameters of an RC. The 'auxiliary method' for detecting GS provides a computationally efficient pre-training test that guides hyperparameterselection. Furthermore, we provide a metric for RC using the reproduction of the input system's Lyapunov exponentsthat demonstrates robustness in prediction.
On the Secret Sharing Scheme Based on Supercodes Decoding

Secret sharing schemes have been studied intensively for the last 20 years, and these schemes have a number of real-world applications. There are a number of approaches to the construction of secret sharing schemes. One of them is based on codes of forward error correction (FEC). In fact, every linear code can be used to construct secret sharing schemes. For instance original Shamir secret sharing scheme is based on erasure decoding of Reed-Solomon codes. One of the main drawbacks of secret sharing schemes based on FEC is a dependence between number of users (participants) and field size of FEC. In this paper we propose a new scheme of secret sharing based on iterative decoding of LDPC codes in terms of supercodes decoding concept. In this scheme a field size can be made arbitrary and independent on the number of participants.
Tacoma Community College Library: Election 2020 : Home

This is a guide to the 2020 General Election - what you need to know to vote, stay informed, and evaluate the news and messaging
From Bads to Beds. Algorithmic Disclosure Regulation

Amidst the growing skepticism surrounding transparency measures, this paper supports that disclosure regulation can play a role in online transactions, if it is designed through algorithms. This requires solving several theoretical legal challenges and bridging technical solutions, making this article contribute to the Law&Tech scholarship. The contribution of this article is threefold. First, it provides a review of an emerging scholarship: the Law&Technology (Law&Tech), which is distinct from the Technology or Innovation Law, the Legal Tech and Empirical Legal Studies. Law&Tech is a new research field that uses tools from Natural Language Processing (NLP) and Machine Learning (ML) to investigate on legal topics. We classify it around four research fields: (i) analysis, (ii) interpretation (iii) application and enforcement, (iv) enhancement. (Part One) Second, the article draws on the fourth (iv) future-oriented sub-category of the Law&Tech literature to demonstrate that research on how algorithms could contribute to enhance the rule- making of disclosure regulation is still limited. It therefore advances the current knowledge on how that would be possible.Third, it presents a new (two-phase) model to draft effective disclosure regulation. In both phases, algorithmic technologies are used, making this contribution fit the Law&Tech scholarship. In phase one, we suggest using Natural Language Processing and Machine Learning technologies to first combine Rules and Industry disclosures (the de iure and de facto disclosures), and then grade them to select those that ‘fail’ the least: the BADs, or Best Available Disclosures. (Part Two)In phase two, we propose using regulatory sandbox to test the BADs and get to the BEDs (or Best Ever Disclosures). Technically speaking, the BEDs would: be adaptive to the addressees’ informational needs (i.e. feed-in through behavioral data); targeted at diversified groups of addressees; cheap to implement. From a legal point of view, testing the messages in the sandbox before implementation serves to grant due process (transparency and participation) of algorithmic rule making; but also to increase BEDs’ proportionality (least possible burdensome solution for recipients) (Part Three).
A Model of Foreign Language Listening Ability Assessment Assisted by Mobile Devices based on Neural Network

In order to overcome the inaccuracy of foreign language listening assessment and the limitations of the assessment process, a model of foreign language listening ability assessment assisted by mobile devices based on neural network is proposed. Aiming at the characteristics of mobile devices, this paper applies artificial intelligence technology to foreign language listening ability assessment, and mining relevant data. BP network learning ability, nonlinear processing ability and fault tolerance ability are set as the assessment indicators of foreign language listening ability. A model of foreign language listening ability assessment assisted by mobile devices based on neural network is established, and the intelligent assessment of foreign language listening ability is realised through the model. The simulation results show that the proposed method can quickly and accurately evaluate the user's foreign language listening ability.
팀 활동을 적용한 사례기반학습이 일반교육 교사의 통합교육 효능감 및 학습 만족도에 미치는 영향

This study was conducted to analyze the effect of case-based learning for students applying team-based activities on teachers’ efficacy in inclusive practice and learning satisfaction. The subjects of this study were 49 graduate students attending the special education major in A National University of Education, and all of them are general education teachers. During the three-hour class in every week, at least one hour was allocated for team-based activities. From week 11, the lecture-style class was reduced 2 to 1 hour so that team-based activities could be more focused, and time was allocated so that team activities could be conducted for 2 hours. Cases include teaching problems of special education students, problem behaviors of students with special needs (including problem behaviors of students without disabilities), parent counseling, and cooperation with special education teachers. As a result of the study, case-based learning for students applying team-based activities has a positive effect on enhancing teachers’ efficacy in inclusive practice, that is, teaching efficacy for students with disabilities, efficacy in managing student behavior, and cooperation with related experts and parents. In addition, learning satisfaction was also high, and teachers who experienced inclusive education showed higher satisfaction statically than teachers who did not experienced inclusive education.
AlertMe

Advances in deep learning have enabled brand new video analytics systems and applications. Existing systems research on real-time video event detection does not consider matching based on natural language; rather, it focuses on using Domain Specific Languages that define spatio-temporal operators on video streams for efficient matching. Alternatively, research in the multimodal AI community on joint understanding of video and language focuses on applications such as language-based video retrieval, where videos may have been processed offline. In this work, we propose AlertMe, a multimodal-based live video trigger system that matches incoming video streams to a set of user-defined natural language triggers. We dynamically select the optimal sliding window size to extract feature vectors from different modalities in near real time. We also describe our approach to achieve on-device deployment by introducing a profiler to select runtime-efficient feature extractors. Lastly, we show that limiting the number of trigger candidates can significantly increase event detection performance in applications such as task following in AR glasses.
Penerapan Metode Pembelajaran Modeling dalam Meningkatkan Kemampuan Menulis Karangan Siswa V Sekolah Dasar

Indonesian language, has three aspects that need to be considered, namely aspects of knowledge (competence), skills and attitudes. Those three aspects are in succession concerning the four skills, namely listening, speaking, reading, and writing. The research method used in this study is a descriptive quality research method. With the study subject of students and teachers of class V MI Diponegoro 03 Karangklesem District South Purwokerto. The analysis of the data used is a thematic technique of analysis by analyzing the data in the form of field records, recordings, documents and interviews results in depth. The result of this study is that the presence of models in learning essay writing material will help students to think critically. Students will be assisted by observing the model provided, so that students better understand the material being taught. Students not only receive information from teachers, but students can also dig into the information of the model provided
Reading Comprehension Profile Among Intermediate Pupils: Basis for an Intervention Program

Factors in reading have a big effects to the child’s reading comprehension. However, there has been a little investigation of the factors in reading comprehension that explains its results. This study was designed to examine the profile of the respondents in each factors. Factors in reading comprehension is categorized into six (6) namely: Working memory, Vocabulary, Prior Knowledge, Word Recognition, Reading Strategies and Motivation to Read.This study used the descriptive research method considering that this investigation focuses and describes the profile of the respondents in factors affecting reading comprehension in which was been participated by 99 respondents of Grades IV, V, and VI of Bungtod Elementary School. The survey questionnaire that has been used to evaluate the profile of the respondents was adapted and revised from the study entitled “Assessing Factors Affecting the Students Reading Comprehension” by Habmatu Walga Adaba (2016). The results of the instrument were combined to get the respondents profile in terms of the factors affecting reading comprehension.The questionnaire is composed of 36 items in all; working memory is composed of 10 items, and vocabulary is composed of 4 items, then prior knowledge is composed of 5 items, moreover word recognition is composed of 4 items, furthermore reading strategies is composed of 6 items , and lastly motivation to read is composed of 7 items. After the retrieval of the data from the respondents, it was treated as follows: in getting the perception of working memory, vocabulary, prior knowledge, word recognition, reading strategies and motivation to read frequency counts, percentage, and means were used and interpreted accordingly to the scale that has been given.Based on the profile of the respondents, it was revealed that the factors that got the highest mean of 3.2 is working memory while the factors that got the lowest mean of 2.8 is motivation to read. Based on the finding of the study it is recommended that pupils may read more learning materials such as books magazines, newspapers, comics, and other printed materials to improve their reading comprehension, so that they may perform better in school. For the parents, they may broaden their understanding about the importance of the profile in reading comprehension among intermediate pupils. It is also a must for teachers to help their pupils to improve their reading comprehension with the use of intervention program.
Apriori Base Efficient Approach for large Database Compressed in Association Mining

Data mining can be viewed as a result of the natural evolution of information technology. Large Amount of data is being using very rapidly in the world. It to be compressed takes much more time and takes lot of effort to process these data for knowledge discovery and decision making. Data compression technique is one of good solutions to be reduce size of data that can be save more time the time of discovering useful knowledge by using appropriate methods data mining approaches which is used to compress the original database into a smaller one and perform the data mining process for compressed transaction such as M2TQT, APRIORI algorithm, TM algorithm, AIS & SETM, CT-Apriori algorithm, CBMine, CT-ITL algorithm, FIUT- Tree. Among the various techniques M2TQT uses the relationship of transactions to merge related transactions and builds a quantification table to prune the candidate item sets which are impossible to become frequent in order to improve the performance of mining association rules. Thus M2TQT is observed to perform better than existing approaches
Community resilience to face flood disaster in the Baleendah Village, Bandung Regency, Indonesia

Abstract This study aims to determine the level of resilience of the people of Baleendah Village in dealing with floods. This research uses a holistic approach that considers all aspects as a whole. The approach known as mixed research methods. The mixed research method strategy used is concurrent triangulation strategy, where researchers collect qualitative and quantitative data together, then compare the two data to find out the difference or a combination. The community resilience variable used in this study refers to guidelines for measuring disaster resilience issued by GOAL (2015) that has 30 variables that are grouped into 5 thematic areas. These variables include knowledge and education; emergency preparedness and response; and risk management and vulnerability reduction. Based on the analysis of 3 variables and 10 aspects of community resilience, it can be concluded that the Baleendah Village community currently has a resilience level at the “RESILIENT” level, which means that the community can adapt and have a solution to overcome the floods. Based on the study findings, there are two main problems in increasing the resilience of the Baleendah Village community, namely access to financial assistance for people who are economically in the low income community category, and sanitation.
Customer Segmentation Using K-Means Algorithm As A Basis For A Marketing Strategy In The Store Rumah Tua VAPE: Customer Segmentation Using K-Means Algorithm As A Basis For A Marketing Strategy In The Store Rumah Tua VAPE

The development of the industrial system at this time more and more. Competition in the business world is increasingly stringent and requires business people to create good marketing strategies, one of which utilizes transaction data from Old Vape store companies is a company engaged in the field of electronic cigarettes. But the lack of observation in choosing customer data is less effective. And to overcome this problem can use data clustering analysis using k-means algorithm. And clustering techniques are functional in data mining, data mining algorithms are grouping a lot of data into several data groups or clusters. The data used is taken from the Vape Old House Store customer data, which results in a type of data grouping, namely customer segmentation for basic strategies in the Vape Old House Store so that customer data is not in vain.
A study on Mamdani fuzzy logic to implement the programs of washing machine

Abstract: Fuzzy set is set with the ambiguity of elements regarding their belongingness. In the washing machine, the dirtiness of clothes, the amount of washing powder, etc. are fuzzy terms. These are operated through fuzzy logic systems. But the operations on the inputs are most important for suitable output. Quality of washing powder is an essential input variable to the current existing inputs like dirtiness, types of cloths etc. This study will focus on the proposed operations on the system of MAMDANI in fuzzy logic for washing machine.
Use of ICT in Teaching Vocational Subjects

In today’s modern world, globalization has completely changed the way of working. The way we live, learn, work, and even define work has changed due to new information and communication technologies—Hence, it can stated that human capital fuel up the modern economy. In reality, the information and communication technology revolution has turned intelligence into a valuable commodity. In today's economy, economic growth is based on mental intelligence rather than physical strength, and its worth is generated by recruiting knowledgeable workers and continuing to learn. Incorporating information and communication technology (ICT) into vocational and technical education and the educational system in general has a vast range of consequences on teaching and learning.
STUDENTS VERBAL COMMUNICATION AN EXPRESSIVE SKILLS AT THE FACULTY OF PEDAGOGY

Communication skills help in many aspects of life such as professional, personal, or different unexpected situations in our life. Good communication skills are essential skills for speaking clearly with others, also helps us to avoid different misconceptions that might happen while speaking. Through the years of studying is very important to create situations in or outside the lecture halls for students to use their communication skills for example: group assignments or class presentations. Therefore, the main purpose of this research is to find the factors that interfere the good communication skills, how able are students in verbal communication, to identify the reasons that interrupt them while communicating, what do they do to avoid the difficulties in speaking, and how the professors take part on all this. A sample of this study includes 90 students and 12 professors of the Faculty of Pedagogy of the University of Tetova. The instrument of the study is a self- administrated questionnaire which is done in google forms with 12 items for students and professors. The results show that shame and doubt are two main factors that interrupt students' communication, while professors think that the main factor is fear, but there are also different thoughts behind fear and doubt, also is present the indifference of the students. Students are conscious that professors appreciate the students that are more communicative and active during lectures.