Reviewerly/exHarmony huggingface dataset:
Classifying Reasonability in Retellings of Personal Events Shared on Social Media: A Preliminary Case Study with /r/AmITheAsshole

People regularly share retellings of their personal events through social media websites to elicit feedback about the reasonability of their actions in the event's context. In this paper, we explore how learning approaches can be used toward the goal of classifying reasonability in personal retellings of events shared on social media. We collect 13,748 community-labeled posts from /r/AmITheAsshole, a subreddit in which Reddit users share retellings of personal events which are voted upon by community members. We build and evaluate a total of 21 machine learning models across seven types of models and three distinct feature sets. We find that our best-performing model can predict the reasonability of a post with an F1 score of .76. Our findings suggest that features derived from the post and author metadata were more predictive than simple linguistic features like the post sentiment and types of words used. We conclude with a discussion on the implications of our findings as they relate to sharing retellings of personal events on social media and beyond.
Increasing Iterate Averaging for Solving Saddle-Point Problems

Many problems in machine learning and game theory can be formulated as saddle-point problems, for which various first-order methods have been developed and proven efficient in practice. Under the general convex-concave assumption, most first-order methods only guarantee an ergodic convergence rate, that is, the uniform averages of the iterates converge at a O(1/T) rate in terms of the saddle-point residual. However, numerically, the iterates themselves can often converge much faster than the uniform averages. This observation motivates increasing averaging schemes that put more weight on later iterates, in contrast to the usual uniform averaging. We show that such increasing averaging schemes, applied to various first-order methods, are able to preserve the O(1/T) convergence rate with no additional assumptions or computational overhead. Extensive numerical experiments on zero-sum game solving, market equilibrium computation and image denoising demonstrate the effectiveness of the proposed schemes. In particular, the increasing averages consistently outperform the uniform averages in all test problems by orders of magnitude. When solving matrix and extensive-form games, increasing averages consistently outperform the last iterates as well. For matrix games, a first-order method equipped with increasing averaging outperforms the highly competitive CFR+ algorithm.
Exploration and Practice of Online-Offline Blended Teaching in College English Courses

With the development of information technology, college English teaching is faced with new challenges, but at the same time an alternative model and platform of teaching is made possible.That is online teaching.This paper elaborates on the necessity of fully harnessing information technology in building diversified teaching environment and implementing online-offline blended teaching.It also illustrates the achievement of the exploration and practice of online-offline blended teaching in the author's own college English course activities.
Are We Ready For Learned Cardinality Estimation

Cardinality estimation is a fundamental but long unresolved problem in query optimization. Recently, multiple papers from different research groups consistently report that learned models have the potential to replace existing cardinality estimators. In this paper, we ask a forward-thinking question: Are we ready to deploy these learned cardinality models in production? Our study consists of three main parts. Firstly, we focus on the static environment (i.e., no data updates) and compare five new learned methods with eight traditional methods on four real-world datasets under a unified workload setting. The results show that learned models are indeed more accurate than traditional methods, but they often suffer from high training and inference costs. Secondly, we explore whether these learned models are ready for dynamic environments (i.e., frequent data updates). We find that they cannot catch up with fast data up-dates and return large errors for different reasons. For less frequent updates, they can perform better but there is no clear winner among themselves. Thirdly, we take a deeper look into learned models and explore when they may go wrong. Our results show that the performance of learned methods can be greatly affected by the changes in correlation, skewness, or domain size. More importantly, their behaviors are much harder to interpret and often unpredictable. Based on these findings, we identify two promising research directions (control the cost of learned models and make learned models trustworthy) and suggest a number of research opportunities. We hope that our study can guide researchers and practitioners to work together to eventually push learned cardinality estimators into real database systems.
An Eminent Spam Noticing Methodology for IOT Gadgets Using ML Techniques

Net of factors (IoT) is also a bunch of numerous sensory gadgets and actuators connected over a wireless or wi-fi channel for statistics transmission. IoT is growing on a everyday for the past few years. The little print are substantially extended inside the upcoming years. Moreover to improved extent, IoT gadgets produce big amounts of information in lots of unique methods with unique statistics first-rate described by their pace through the years and dependence. The gadget gaining knowledge of (ml) algorithm plays an vital role in protection and authorization supported the invention of artificial biotechnology to enhance the utilization and security of IoT structures. Attackers frequently looked for gaining knowledge of algorithms to need advantage of being susceptible to IoT systems designed for smart. Protection for IoT gadgets by way of detecting spam the usage of ml. Unsolicited mail detection on IoT employing a system getting to know Framework is proposed.
Enhancement of cloud performance metrics using dynamic degree memory balanced allocation algorithm

In cloud computing, load balancing among the resources is required to schedule a task, which is a key challenge. This paper proposes a dynamic degree memory balanced allocation (D2MBA) algorithm which allocate virtual machine (VM) to a best suitable host, based on availability of random-access memory (RAM) and microprocessor without interlocked pipelined stages (MIPS) of host and allocate task to a best suitable VM by considering balanced condition of VM. The proposed D2MBA algorithm has been simulated using a simulation tool CloudSim by varying number of tasks and keeping number of VMs constant and vice versa. The D2MBA algorithm is compared with the other load balancing algorithms viz. Round Robin (RR) and dynamic degree balance with central processing unit (CPU) based (D2B_CPU based) with respect to performance parameters such as execution cost, degree of imbalance and makespan time. It is found that the D2MBA algorithm has a large reduction in the performance parameters such as execution cost, degree of imbalance and makespan time as compared with RR and D2B CPU based algorithms
Entity Enhancement for Implicit Discourse Relation Classification in the Biomedical Domain

Implicit discourse relation classification is a challenging task, in particular when the text domain is different from the standard Penn Discourse Treebank (PDTB;Prasad et al., 2008) training corpus domain (Wall Street Journal in 1990s).We here tackle the task of implicit discourse relation classification on the biomedical domain, for which the Biomedical Discourse Relation Bank (BioDRB; Prasad et al., 2011) is available.We show that entity information can be used to improve discourse relational argument representation.In a first step, we show that explicitly marked instances that are content-wise similar to the target relations can be used to achieve good performance in the cross-domain setting using a simple unsupervised voting pipeline.As a further step, we show that with the linked entity information from the first step, a transformer which is augmented with entity-related information (K-BERT; Liu et al., 2020) sets the new state of the art performance on the dataset, outperforming the large pre-trained BioBERT (Lee et al., 2020) model by 2% points.
A Python-WSGI and PHP-Apache Web Server Performance Analysis by Search Page Generator (SPG)

The web servers (WSGI-Python) and (PHP-Apache) are in middleware tier architecture. Middleware architecture is between frontend tier and backend tier, otherwise it’s a connection between frontend tier and backend tier for three tier architecture. The ELearning systems are designed by two different dynamic web technologies. First is by Python-WSGI and the second is by Personal Home Page (PHP-Apache). The two websites were designed with different open source and cross platform web technologies programming language namely; Python and PHP in the same structure and weight will evaluate perform over two different operating systems (OSs): 1) Windows-16 and 2) Linux-Ubuntu 20.4. Both systems run over the same computer architecture (64bit) as a server side with a common backend MySQL web database for both of them. Nevertheless, the middleware for PHP is a cross Apache MySQL PHP Perl (XAMPP), but the middleware for Python is Pycharm and the web server gateway interface (WSGI). WSGI and Apache are both web servers and this paper will show which of them has a better response time (RT). On the one hand, the experimental results demonstrate that the Python-WSGI is even weightier in Mbyte than PHP-Apache, on the other hand Python is still faster and more accurate than PHP. The designed SPG is by handwriting codes: one time designed the SPG by PHP source code and the other time designed by Python source code. Both Python-WSGI and PHP-Apache results are targeted to compare by the least time in milliseconds and take in to account enhanced performance.
Revisiting smart contract vulnerabilities in Hyperledger Fabric

Hyperledger Fabric is a permissioned enterprise blockchain allowing organizations to collaborate and automate processes via smart contracts. However, these contracts could contain security vulnerabilities leading to unexpected behavior or other negative consequences. Therefore, this study takes a closer look at three reported smart contract vulnerabilities in Fabric: rich queries, pseudorandom number generators, and global variables. Smart contracts containing these vulnerabilities were deployed on a test network, and the vulnerable contract features were exploited and explained. The study provides an estimation of each vulnerability's impact severity, and possible countermeasures to lower it were explored and evaluated. This study found that the proposed countermeasures can at least mitigate the impact severity of all three vulnerabilities. Additionally, the study provides an overview of compatible analysis tools. The available tools were found to be lacking, however, as most of them do not exist outside of research papers. Overall, static code analysis tools were found to be effective at detecting all three vulnerabilities.
On the Rise and Fall of Simple Stupid Bugs: a Life-Cycle Analysis of SStuBs

Bug detection and prevention is one of the most important goals of software quality assurance. Nowadays, many of the major problems faced by developers can be detected or even fixed fully or partially with automatic tools. However, recent works explored that there exists a substantial amount of simple yet very annoying errors in code-bases, which are easy to fix, but hard to detect as they do not hinder the functionality of the given product in a major way. Programmers introduce such errors accidentally, mostly due to inattention.Using the ManySStuBs4J dataset, which contains many simple, stupid bugs, found in GitHub repositories written in the Java programming language, we investigated the history of such bugs. We were interested in properties such as: How long do such bugs stay unnoticed in code-bases? Whether they are typically fixed by the same developer who introduced them? Are they introduced with the addition of new code or caused more by careless modification of existing code? We found that most of such stupid bugs lurk in the code for a long time before they get removed. We noticed that the developer who made the mistake seems to find a solution faster, however less then half of SStuBs are fixed by the same person. We also examined PMD's performance when to came to flagging lines containing SStuBs, and found that similarly to SpotBugs, it is insufficient when it comes to finding these types of errors. Examining the life-cycle of such bugs allows us to better understand their nature and adjust our development processes and quality assurance methods to better support avoiding them.
Post-Editing Oriented Human Quality Evaluation of Neural Machine Translation in Translator Training: A Study on Perceived Difficulties and Benefits

The aim of this article is to investigate translation trainees' perceived difficulties and benefits of a post-editing oriented neural machine translation (NMT) error annotation and quality evaluation task which was carried out for the language pair English-Turkish and for two separate text types and domains, i.e., environmental blogs and movie/TV reviews, within the scope of an MA course on translation quality standards.The data to be analyzed were collected through a semi-structured questionnaire which was given to the trainees attending the course after the completion of the task.The questionnaire was prepared with the aim of understanding perceived difficulties and benefits of the task.Analysis of the answers revealed that most of the trainees were in the opinion that the task was difficult.Majority of the trainees also believed that the task was beneficial and enabled them to feel empowered to make decisions on human translation quality evaluation of machine translation (MT) and to carry out error annotation and post-editing activities in the future.According to a significant number of trainees, error annotation facilitated their post-editing process and reduced the effort in post-editing.Enhanced understanding of MT error annotation and enhanced ability to perform post-editing were the significant benefits stated by the trainees.The difficulties were associated with being introduced and assigned to perform tasks they were not familiar with.Yet, as displayed in the answers to the questionnaire, a considerable majority of the trainees were positive about the learning experience.The results have also shown that integration of MT-related activities into translator training with a focus on the empowerment of the human translator has its difficulties and benefits also for translation trainers.While the difficulties for the trainers concern the decisions on the design, implementation, and planning of the task and the responsibility to carry out the task in constant interaction and collaboration with the trainees, the benefits are the sense of fulfillment and enrichment brought by positive feedback from the trainees and the discovery of the fact that the so-called 'teaching experience' becomes a 'learning experience' for trainers as well as trainees.
Student’s Media Literacy Training As A Factor Of Personality Development In The Inclusive Education

Media literacy development is crucial for socialization and personal development in current circumstances.Media literacy allows to safely use media space, to critically question the incoming media information, not to get exposed to fraud or disinformation.The use of digital technologies in the inclusive education gives the opportunity to enlarge the media use for educational purposes as well as for creating the environment for schoolchildren to produce their own media products.The Internet communication skills help people to be more viable in the labour market.It is important to develop media literacy since the school days.The Federal Law of the Russian Federation on education states the notion of integrated (inclusive) education which allows the children with special needs acquire proper education.By the time young people graduate from school they are to have certain skills, possess required knowledge and capabilities to live self-sufficient lives in the future.The inclusive education should contribute to acquiring these skills including media literacy.The author gives the definition of the notion "media literacy" for a modern school child, shows the importance of media literacy for the student's personality irrespective of their health condition and states the difficulties a teacher is about to face when working with children with special needs (teaching media literacy).The model of media literacy training for the school children in the context of inclusive education digitalization is presented in the article.
Development of a Prosodic Read Speech Syllabic Corpus of the Yoruba Language

Literature revealed that the need for annotated database of speech text or audio files is justified primarily by the requirements for corporal entities to conduct basic Natural Language Processing (NLP) studies on a language.Such investigationstraverse thephonetic, aural and etymological representations of the language.Moreover, research of interest can also span grammatic, semantic, pragmatic and syntactic characterizations of the particular language.At a secondary level an annotated speech corpus is desirable for the purpose of speech synthesistypified by Text-to-Speech (TTS) and recognitionas in Speech-to-Text (STT).Yoruba language, a resource scarce language with a wide usage,has sparse andscarce digital resources and its computerization poses unique challenges.Annotated speech corpus is one of such resources.Hence, this research was motivated by the need to contribute to the scanty resources for the language.This research minedtextual inputs from four sources including two Standard Yoruba (SY) fiction, an SY grammar textbook and an SY Online Scripture.A hybrid of Falaschi scheme and the add-on procedure of Radová and Vopálka were applied to extractphonetically balanced text bag of 7376 phrases and sentences with a view to minimizing the extraction cost, while maximizing phonetic coverage of all standard Yoruba syllabic events.The selected text was read by an expert and recorded in a suitable environment and saved as wave files.The wave files were annotated with Praat.A relational database was developed to host the corpus metadata.The corpus performed impressively when tested with a Standard Yoruba TTS.This paper presents the design, implementation, results and other useful information about the research.
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation

Table-to-text generation refers to generating a descriptive text from a key-value table.Traditional autoregressive methods, though can generate text with high fluency, suffer from low coverage and poor faithfulness problems.To mitigate these problems, we propose a novel Skeleton-based two-stage method that combines both Autoregressive and Non-Autoregressive generation (SANA).Our approach includes: (1) skeleton generation with an autoregressive pointer network to select key tokens from the source table ; (2) edit-based non-autoregressive generation model to produce texts via iterative insertion and deletion operations.By integrating hard constraints from the skeleton, the non-autoregressive model improves the generation's coverage over the source table and thus enhances its faithfulness.We conduct experiments on both the WikiPerson and WikiBio datasets.Experimental results demonstrate that our method outperforms the previous state-of-the-art methods in both automatic and human evaluation, especially on coverage and faithfulness.In particular, we achieve PARENT-T recall of 99.47 in WikiPerson, improving over the existing best results by more than 10 points.
BLOCKCHAIN IN EDUCATION: HOW TO MANAGE STUDENT CREDITS OF HIGHER EDUCATION THROUGH THE BLOCKCHAIN?

Currently, technologies are developing very quickly and the need for information security is constantly increasing. In this connection, Blockchain technology is becoming in demand, which allows us to keep information safety and integrity. In addition, the technology enables the creation of a decentralized environment where transactions and data are take place without any third party organization. We proposed a decentralized web resource based on the Ethereum platform for managing student credits. The decentralized application (Dapp), will process, manage and control tokens, which represent credits that students gain for completed certain courses. The credit system is a first step towards a more transparent and technologically advanced form which could be used by universities and students to manage credits. The novelty of this scientific research is the creation of a web-based information resource based on Blockchain technology. Thanks to this resource, it becomes possible to track students' grades and receive reliable information about higher education. This completely eliminates the possibility of making changes to existing records.
WIND: Weighting Instances Differentially for Model-Agnostic Domain Adaptation

Domain Adaptation is a fundamental problem in machine learning and natural language processing.In this paper, we study the domain adaptation problem from the perspective of instance weighting.Conventional instance weighting approaches cannot learn the weights which make the model generalize well in target domain.To tackle this problem, inspired by meta-learning, we formulate the domain adaptation problem as a bi-level optimization problem, and propose a novel differentiable modelagnostic instance weighting algorithm.Our proposed approach can automatically learn the instance weights instead of using manually designed weighting metrics.To reduce the computational complexity, we adopt the secondorder approximation technique during training.Experimental results 1 on three different NLP tasks (Sentiment Classification, Neural Machine Translation and Relation Extraction) illustrate the efficacy of our proposed method.
Abusive Language and Hate Speech Detection for Indonesian-Local Language in Social Media Text

In social media, people are free to express their feelings and thoughts. However, people can also use abusive language and hate speech to insult or humiliate individuals or groups on social media, such as Twitter. Various detection methods have been developed to control the spread of abusive language and hate speech in Indonesia, but the detection process is still focused on monolingual. As a country with various ethnicities and cultures, Indonesia also has a variety of local languages. This study examines abusive language and hate speech detection on Twitter, which also contains five local languages, including Javanese, Sundanese, Madurese, Minangkabau, and Musi. In this work, we present a preliminary evaluation to find the best performance of machine learning methods in detecting abusive language and hate speech on Twitter as preliminary study for each local language. We use several machine learning algorithms, such as Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest Decision Tree (RFDT) as classifiers and TF-IDF weighted word n-gram and character-n gram as feature extraction. The experiments use the 5-Fold cross-validation approach and evaluated by measuring the F-1-Score. After the experiment, we have obtained the SVM classifier with word n-gram features show the best F-1-Score for each dataset.