Reviewerly/exHarmony huggingface dataset:
Brand Potential User Identification Algorithm Based on Sentiment Analysis

This paper firstly compares the current research status of text sentiment analysis and potential customer identification, and introduces the process of building sentiment dictionaries and feature selection, feature screening, and common classification algorithms in text analysis. Secondly, around the most used tool for sentiment analysis, sentiment dictionary, the sentiment polarity discriminative rules of sentiment words are studied. In response to the shortcomings of using a single recognition algorithm in the current process of building sentiment dictionaries, an improved integration rule is designed and an automatic construction method for domain sentiment dictionaries in the social media environment is proposed. Then, this paper analyzes the sentiment topic information existing in user-generated content and adds the domain sentiment lexicon to the joint sentiment topic model as a posteriori information to extract the sentiment topic features, based on which the feature engineering study of potential customer identification is conducted and the feature set is constructed. In addition, a sample resampling method and a diverse integration framework for unbalanced data are designed to work together for the prospect identification task under data skewing in response to the category imbalance in real data. Finally, an experimental study is conducted using a social media text corpus to validate the proposed method in this paper. The proposed domain sentiment lexicon construction method and the joint domain sentiment topic-based lead identification method show good performance in different control group experiments. This paper provides an in-depth study on the construction of domain sentiment lexicon and imbalance classification in theory and provides solutions for companies to discover potential customers in practice, which has certain theoretical significance and practical value.
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization

We analyze the convergence rates of stochastic gradient algorithms for smooth finite-sum minimax optimization and show that, for many such algorithms, sampling the data points without replacement leads to faster convergence compared to sampling with replacement. For the smooth and strongly convex-strongly concave setting, we consider gradient descent ascent and the proximal point method, and present a unified analysis of two popular without-replacement sampling strategies, namely Random Reshuffling (RR), which shuffles the data every epoch, and Single Shuffling or Shuffle Once (SO), which shuffles only at the beginning. We obtain tight convergence rates for RR and SO and demonstrate that these strategies lead to faster convergence than uniform sampling. Moving beyond convexity, we obtain similar results for smooth nonconvex-nonconcave objectives satisfying a two-sided Polyak-{\L}ojasiewicz inequality. Finally, we demonstrate that our techniques are general enough to analyze the effect of data-ordering attacks, where an adversary manipulates the order in which data points are supplied to the optimizer. Our analysis also recovers tight rates for the incremental gradient method, where the data points are not shuffled at all.
Real Time Web Based Multilingual Chat Application

Social networking sites helps the users to communicate with others over internet irrespective of the place they stay. So, the application should be real-time and platform independent. These days, a chat application with Multilanguage support and more features like organizing the chats into channels and categories will be very useful for community/company which are located in different countries. Currently, there are applications with translation features, but the real-time & organized multilingual web application will be most effective and useful. Digitisation in India has witnessed rise of internet users to a vast extinct where most users are native language speakers than those who speaks English in the country. An application that does not support multilingual features, will get bound only to a particular region or area. One cannot widen their connections, get new customers or chat with the people across the world. The development of chat-based services in the different parts of the world who speak different languages rapidly becomes one of the reasons to build this multilingual chat application which would help to remove the language barrier.
An Multi-Dimensional Video Reverse Search Engine using Computer Vision and Machine Learning

Online media has become a mainstream of current society. With the rapid development of video data, how to acquire desired information from certain provided media is an urgent problem nowadays. The focus of this paper is to analyse a sufficient algorithm to address the issue of dynamic complex movie classification. This paper briefly demonstrates three major methods to acquire data and information from movies, including image classification, object detection, and audio classification. Its purpose is to allow the computer to analyse the content inside of each movie and understand video content. Movie classification has high research and application value. By implementing described methods, finding the most efficient methods to classify movies is the purpose of this paper. It is foreseeable that certain methods may have advantages over others when the clips are more special than others in some way, such as the audio has several significant peaks and the video has more content than others. This research aims to find a middle ground between accuracy and efficiency to optimize the outcome.
SURVEY PAPER ON REAL TIME SIGN LANGUAGE INTERPRETER

Deaf and Dumb people use sign language to communicate. There are various sign recognition techniques that produce output in the form of words or identified signs. The suggested method focuses on Sign Language Interpretation in correct sentences .In addition to sign identification several NLP (Natural Language Processing) techniques are applied .Input is given as video of sign language followed by framing and segmentation on video. Deaf people get isolated as result of this. However, if an android application can be developed to convert sign language into written and audio format, the gap between normal people and the deaf community can be narrowed. The HAAR CASCADE classifier is used to identify signs. The continuous words for each sign are sent as input to the POS (Part of Speech) tagging module after sign recognition. It is utilized a word net POS tagger with its own word net dictionary. Finally, the sentence is framed using the LALR Parser. In this approach the suggested sign language Interpreter model produces intelligible sentence. This sentence is again converted into audio using gTTS API .We will be developing an android application which will scan the sign languages and interpret it into specific sentence and audio using which a normal person can understand the language of Deaf and Dumb People.
TYPES OF DEIXIS USED IN THE DOUM-TREE OF WAD HAMID: A CORPUS-BASED STUDY

In linguistics, the term deixis is used to describe the use of general words and phrases by speakers to point to persons, objects, specific place and time from their physical or psychological positions while communicating their messages. Such words and phrases are considered deictic when their denoted meaning becomes context-dependent. The present study used AntConc v.3.5.9 corpus analysis toolkit to find out the frequent occurrence of the types of deixis used in the English translation of the short story The Doum-tree of Wad Hamid, and then utilized the useful information obtained along with examples manually derived from the story to discuss the extent to which this use of deixis reflects the author's style and the story's point of view. The results indicated that the common five types of deixis were used in the short story with a different degree of frequency and usage, but personal deixis was the most dominant with 653 examples represented 54.10% out of the total of 1207 deictic instances found in the corpus. Social deixis ranks second with 242 examples represented 20.05%. The study deduced that the dominant use of person pronouns as personal deixis or relational social deictic words reflects the author's style and point of view in the narration line via which the narrator recounts the events from his own perspective.
The Effectiveness of Combining Scripting and Talking Chips on the Students’ Speaking Achievement

This research is to find the effectiveness of combining Scriting and Talking Chips which helps students to be more active in speaking class. This research used quasi-experimental research with non-randomized pretest posttest design. The students of English Department of UNIPA Campus Blitar who took Basic Speaking 1 were the subjects of this research, in which Class A as the experimental group and class B as the control group. The instruments of this research were speaking test (pretest and posttest). One group was taught by combining scripting and talking chips method, in contrast, the control group was taught by using direct method. Then, both groups were given a posttest. The data was analyzed using ANCOVA and computed by SPSS version 20. The result of the analysis indicated that combining Scripting and Talking Chips strategy could make students’ speaking achievement better.
Digital Transformation of the Learning Process - Students' Attitudes about Online Teaching

The COVID-19 pandemic has accelerated the process of digital transformation of higher education institutions. In a very short period, teachers and students abruptly switched to digital environments, which they had not used until then. As online teaching is very different from traditional teaching, teachers and students are faced with numerous new challenges. Online teaching requires a specific environment that primarily implies the availability of adequate technology as well as the skills that both teachers and students should have. Some higher education institutions have completely switched to online mode, while others have practiced a combined (online and offline) mode. The aim of this paper is, based on a questionnaire developed by Bernard et al. (2007), to examine the level of online skills, readiness for online learning and learning initiatives, attitudes about online learning, as well as the desire for online interaction with teachers and colleagues by the surveyed students.
OCHADAI at SemEval-2022 Task 2: Adversarial Training for Multilingual Idiomaticity Detection

We propose a multilingual adversarial training model for determining whether a sentence contains an idiomatic expression.Given that a key challenge with this task is the limited size of annotated data, our model relies on pre-trained contextual representations from different multilingual state-of-the-art transformer-based language models (i.e., multilingual BERT and XLM-RoBERTa), and on adversarial training, a training method for further enhancing model generalization and robustness.Without relying on any human-crafted features, knowledge bases, or additional datasets other than the target datasets, our model achieved competitive results and ranked 6 th place in SubTask A (zeroshot) setting and 15 th place in SubTask A (oneshot) setting.
An Improved NLP for Syntactic and Semantic Matching using Bidirectional LSTM and Attention Mechanism

Dealing with extensive amounts of textual data requires an efficient deep learning model to be adapted. However, the following reasons; the highly ambiguous and complex nature of many prosodic phrasing also enough dataset suitable for system training is always limited, cause big challenges for training the NLP models. This proposed conceptual framework aims to provide an understanding and familiarity with the elements of modern deep learning networks for NLP use. In this design, the encoder uses Bidirectional Long Short-Term Memory deep network layers, to encode the test sequences into more context-sensitive representations. Moreover, the attention mechanism is mainly used to generate a context vector that is determined from distinct alignment scores at different word positions, hence, it can focus more on a small words' subset. Hence, the attention mechanism improved the model data efficiency, and the model performance is validated using an example of data sets that show promise for a real-life application.
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation

Sparse Gaussian Processes are a key component of high-throughput Bayesian optimisation (BO) loops -- an increasingly common setting where evaluation budgets are large and highly parallelised. By using representative subsets of the available data to build approximate posteriors, sparse models dramatically reduce the computational costs of surrogate modelling by relying on a small set of pseudo-observations, the so-called inducing points, in lieu of the full data set. However, current approaches to design inducing points are not appropriate within BO loops as they seek to reduce global uncertainty in the objective function. Thus, the high-fidelity modelling of promising and data-dense regions required for precise optimisation is sacrificed and computational resources are instead wasted on modelling areas of the space already known to be sub-optimal. Inspired by entropy-based BO methods, we propose a novel inducing point design that uses a principled information-theoretic criterion to select inducing points. By choosing inducing points to maximally reduce both global uncertainty and uncertainty in the maximum value of the objective function, we build surrogate models able to support high-precision high-throughput BO.
Influence of physical tool attributes on presence in virtual learning environments

Virtual reality offers new opportunities in production education and training. The simulation of haptic perception is, however, still an ongoing topic of research. This paper describes the development of a simple tool replica to simulate different hand-held tools out of the production. Different attributes like shape, weight and functionality of the replica are changed during a user study with 20 participants. The task within the virtual environment contains a simple screw driving scenario. The test runs compared on the resulting sense of presence, since it has already be shown that presence has an impact on the learning success. The results show significant effects especially in the subscale of experienced realism. The greatest effects are measured with all attributes integrated. However, an additional integration of a single attribute like realistic weight or functionality does not necessary lead to a significant increasing sense of presence.
Penerapan Teknik Forward Chaining dan Certainty Factor Untuk Mendeteksi Penyakit Gastroesophageal Reflux Disease (GERD)

In this day and age, the lifestyle of people who always want to be practical and always delay eating time causes the body susceptible to disease, this unhealthy habit that makes the stomach produce excess acid that triggers the onset of Gastroesophageal Reflux Disease (GERD). Gerd disease is often considered mild by the public, but if left unchecked then the disease becomes very dangerous because it can irritate the lining of the esophagus and make it become inflamed. Over time, inflammation can damage the lining of the esophagus causing complications such as bleeding, narrowing of the esophagus, and can attack other organs of the body. Early detection of GERD in the stomach needs to be done to prevent the disease. But because of the lack of public knowledge about the symptoms of this disease and the frequent errors in diagnosing GERD, therefore it is necessary to build an expert system to diagnose GERD-based web. Research conducted by Bandar Pasir Mandoge Health Center because GERD disease is a type of disease that is often experienced by the community, and this type of disease is a disease that has the most number of patients in Bandar Pasir Mandoge Health Center with the number of patients as many as 534 in 2021. The purpose of this study is not to replace the role of humans, but to sub substitution human knowledge into the form of a system, so that it can be used by many people (experts and the general public) in dealing with GERD disease. In the preparation of expert systems, a combination of inference rules with a certain knowledge base is provided by one or more experts in a particular field. Several methods can be used, one of which is Forward Chaining which is a reasoning that starts from the facts to get the conclusion of the fact. The results of web-based expert system research using this forward chaining method can act as an expert to detect types of GERD disease and advice for prevention
KNOWLEDGE RISKS AND SUSTAINABILITY: AN EMPIRICAL STUDY ON LEBANESE KNOWLEDGE-INTENSIVE FIRMS

Today’s business environment is characterized by a growing number of knowledge risks. There has been a paucity of empirical research on the impact of knowledge risks on business sustainability. This paper looks at the direct and indirect consequences of knowledge risks on a firm's sustainability. A questionnaire was administered with a sample of 427 respondents from Lebanese knowledge-intensive firms. Structural equation modeling (SEM) was used to test the research framework. The findings indicate that knowledge risks and knowledge loss have a direct and significant detrimental impact on business sustainability. The performance of organizations has a direct and significant positive impact on sustainability. The mediating effect of organizational performance in the relationship between knowledge risks and sustainability of the business was demonstrated. However, the link between human knowledge risks and both organizational performance and sustainability was not significant. Outcomes of the study will persuade knowledge-intensive firms’ managers to deploy knowledge management approaches and reduce potential knowledge risks.
Should I Mine or Should I Break: On the Worthiness of Brute-Forcing Cryptocurrency Addresses

Cryptocurrencies are digital assets that can be used as a medium of exchange and are stored in a ledger in the form of a cryptography-secured computer database. Most cryptocurrencies are deployed in decentralized networks such as the blockchain, where they can be transferred from an address to another. However, anyone having access to the private key of an address can fully operate and steal the full balance of such address. Although brute-force attacks consume significant resources in terms of time and power, breaking one of the richest cryptocurrency addresses leads to a huge reward in terms of hard money. Therefore, an attacker may be incentivized to run this attack.In this paper, we run a statistical study of cryptocurrencies distribution over the richest addresses and assess the worthiness of a brute-force attack towards their public key. We consider an attacker investing a certain amount of hard money (i.e., a currency that is made up of or directly backed by a valuable commodity such as gold) to run the attack and propose a statistical characterization of the average gained reward. To further assess the worthiness of a brute-forcing attack, we compare the average reward with that of other probabilistic reward systems, such as mining and scratch cards. Results show that the average reward of the proposed attack is six orders of magnitude lower than that of mining and 10 orders of magnitude lower than that of scratching cards for a given initial investment.
Dynamic Interaction Between Saving, Investment and Economic Growth in Ethiopia

The study investigates the dynamic interaction among domestic savings, investment and economic growth in Ethiopia with in the period 1992/93 to 2019/20 by utilizing quarterly time series data obtained from National Bank of Ethiopia (NBE). The Vector Error Correction Model (CVECM) results indicated that there was a positive long run relationship among savings, investment and economic growth in Ethiopia. Granger causality from domestic investment and real GDP to domestic savings and domestic investment granger causes economic growth. The response of domestic investment to a positive shock of real GDP is positive. Investment and money supply responds positively to shocks of domestic saving. Exchange rate responds negatively to shock of investment. In the short run the reaction of saving to shocks of real GDP and investment was positive and negative respectively. While in the long run the effect becomes vice versa. Shocks of saving generate negative effect in the short run and insignificant effect on economic growth in the long run. Real GDP responds negatively to shocks of interest rate and exchange rate. Besides, the variance decomposition results show that the variation of economic growth is largely explained by shocks to itself and investment. The variation in saving emanates from money supply, domestic savings, real GDP and investment. Investment deviation is also emanated from Real GDP and saving. Thus, government and stakeholders are supposed to practice macroeconomic policies that will promote economic growth and gross domestic investment and thus saving will increase Keywords: Dynamic, GDP, Saving, Investment, Economic, Growth, Ethiopia DOI: 10.7176/JESD/13-9-04 Publication date: May 31 st 2022
STUDY OF METHODS OF EXTRACURRICULAR WORK IN SYSTEMS OF PREPARATION OF THE FUTURE TEACHERS OF BIOLOGY

We propose to introduce the course „Methods of teaching Biology out-of-class activity” and also fulfillment of practical and scientific research tasks during teaching practice of students in the secondary school with the aim to improve the future teachers' preparing for holding o f out-of class activity in Biology.
Peran Guru dalam Pemanfaatan Media WAG untuk Meningkatkan Pembelajaran di Masa Pandemi Covid-19

The COVID-19 pandemic has affected all aspects of life. One of them is in the field of education. Teachers must be able to use technology to improve learning during the COVID-19 pandemic. Learning activities must continue to prioritize educational goals, namely integrating knowledge, skills, attitudes, and mastery of technology. Online learning is an alternative that can overcome these problems. In the midst of the Covid-19 pandemic, teaching and learning activities are endeavored to use innovative online learning media. Various media can be used to carry out online learning. This study aims to explain the role of teachers in the use of online learning technology during the Covid-19 pandemic. This study uses a qualitative approach. The research method used is literature study. The results showed that the teacher's role in the use of WhatsApp Group media could improve learning during the Covid-19 pandemic. In an effort to support the continuity of the education and learning process, WhatsApp Group can be used as a learning medium, so that learning can take place without being limited by space and time. WhatsApp Group is one of the easy online learning media and provides online classes through the group chat feature. By using WhatsApp Group educators can provide encouragement to arouse, stimulate, and increase students' learning motivation, so that learning objectives can be achieved properly. . Through the WhatsApp Group application, a teacher can provide material briefly to students.
Effect of matrix sparsity and quantum noise on quantum random walk linear solvers

We study the effects of quantum noise in hybrid quantum-classical solver for sparse systems of linear equations using quantum random walks, applied to stoquastic Hamiltonian matrices. In an ideal noiseless quantum computer, sparse matrices achieve solution vectors with lower relative error than dense matrices. However, we find quantum noise reverses this effect, with overall error increasing as sparsity increases. We identify invalid quantum random walks as the cause of this increased error and propose a revised linear solver algorithm which improves accuracy by mitigating these invalid walks.