Reviewerly/exHarmony huggingface dataset:
A click-through rate prediction model incorporating user review text and behavior sequence

There is a high correlation between user behavior and user features in recommender systems. User review texts reflect user preferences and item feature information. However, the current research on CTR prediction models based on user behaviors fails to fully mine user features. As a result, the prediction accuracy of the model is not high. To solve this problem, we propose a click-through rate prediction model that fuses user comment text and behavior sequence. The model uses a text convolutional neural network to extract the features of user review text to obtain the feature vector of user comment text, and uses an attention mechanism to capture the user's interest points from the user's behavior sequence to obtain the user's interest feature vector. A multi-layer perceptron is then used to fuse the user's comment text feature vector, interest feature vector and item feature vector for click-through rate prediction. The experimental results show that the proposed model has better prediction performance than current click-through rate prediction models.
"Significant Use of Social Media in Libraries in Present Context - A Theoretical Assimilation "

In the information age, having a variety of strong social ties is becoming increasingly crucial for individuals, in addition to technical expertise and access to information technologies. Libraries are modifying their offerings to support online messaging platforms and advance this new partnership. The purpose of social media is to deliver pertinent and helpful information about products, services, and activities that benefit the local community. The purpose of the academic library's use of social media is to build a more robust and knowledgeable learning community. This essay looks at how libraries might use social media and networking to offer engaging library services in the face of India's deteriorating economic conditions. Without a question, the tremendous technological development of the twenty-first century has had an impact on library services throughout the world, but especially in India. With social networks like Facebook, MySpace, Flickr, YouTube, and Library of Congress, the Social Media boom has slowly permeated the field of librarianship. However, it has become clear that in order to fulfill the expanding expectations of our end consumers, our services will need to adapt. India's libraries are being pushed harder than ever to provide more proactive and value-added services in order to fulfill the constantly changing needs of its users. Therefore, this essay aims to investigate the current situation in library service delivery using this cutting-edge technology.Investigation of the difficulties Indian libraries has using these Social Media and suggestions for solutions are made. In addition, the article attempted to analyze the idea of social networking and its use to advertise library services in the digital age. For social media to be a great experience for everyone, academic institutions will need a best practice social media policy to inform and direct acceptable online behavior and activity.
CLOUD COMPUTING AND SECURITY MODEL- A BRIEF SURVEY

Cloud native architecture and specifically it relates to 5g technology it's awesome user's get more bandwidth get more speed there's a better user experience there's ultralow latency and so there's all these great things that come with 5g and service providers specifically are enabling their large customers with the capabilities with the promise of 5g but in order to do that need to look at having a common architecture defined from the core of network all the way out to the far edge of network and that common architecture is going to allow for faster deployments greater efficiencies reduced operational costs so a lot of benefits to that and the success of 5g capabilities with respect to that common architecture that starts with the infrastructure itself and the infrastructure needs to be cloud cloud-native container-based infrastructure this is going to support the service-based architectures that are defined in the specifications for 5g right service providers want to be able to do is deliver individual capabilities as separate services and in order to do that need the cloud that's offered in the cloud user have got to move to cloud native architectures in order to achieve that.
Increasing Digital Literacy in Realizing Golden Indonesia

It is diaccompanied by the condition of Indonesia where the literature movement in the ability of students to read in the world of education and society is still relatively minimal. One that can be done through the school literacy movement is one form of government awareness of the importance of building a culture of literacy in the world of education in order to create a culture of reading and writing in the school environment as an effort to realize lifelong education. The main and most basic activities are directed at increasing interest in reading. The values of character education can be conveyed implicitly in every learning process, especially in Indonesian language learning. The use of digital literacy is also able to lead students to be able to capture and retrieve information from the internet with teacher monitoring. It is able to broaden students horizons in taking knowledge broadly and facilitate students to be creative freely with maximum monitoring and supervision.
An Exploration into the Students’ Perceptions of Teachers’ Objectivity During Exams Grading: A Case of University of Swat

Examinations are students’ chance for show casing their learning to the examiners and to their teachers for the efforts that were put into their (Teachers) teaching for maximum benefits of the students. However, students, expectations about the gradings are not always fulfilled and students feel that they were either not awarded according to their abilities or they were discriminated against. Reasons of discrimination that students cite and have also been endorsed by different research in different parts of the world include; gender discrimination, discrimination on the basis of students, caste, creed and colour. Some research also links students’ agreeable personality and submissive attitude in presence of the teachers as reasons for favoritism towards some students and discriminating against others beside teachers’ mood swings during students grading, Dee and Gershenson (2017), by Fan et al (2019), Megalokonomou (2021), Terrier (2015) and Bygren (2020). The current research is focused on finding students perceptions about teachers, objectivity and biases students, exams grading through a qualitative case study. Ten (10) participants with equal gender distribution agreed for taking part in the study and for sharing their perceptions about teachers, teachers grading bias or objectivity. This, however, must be noted that the current research study is purely based on only students’ perceptions. Research results show that majority of the participants are of the view that teachers grading has the element of bias on the basis of students’ gender, behaviour, personality and their attachment or friendship with teachers.
Indigenous CHI Workshop

This CHI workshop looked to connect indigenous peoples to surfaces and spaces using a media channel of experience to visit historical narrative, heritage and/or interactive and immersive media applications. The workshop was especially interested in contributions that displayed cultural resilience inside a digital environment or aligned with a common interest to challenge the status quo. We welcomed individuals or groups interested in submitting (but not limited to) interactive design, prototyping, methodology, human-computer interaction, conceptual works that progress indigenous ideas and creative works within a technical lens.
The Development of Minimum Competency Assessment Test Instruments (AKM) for Elementary Schools Contain Reading Literacy Based on the PISA Framework

This research was conducted based on the results of observations and interviews on the lack of teacher involvement in the process of preparing the minimum ability assessment test instrument (AKM) and many teachers and prospective teachers do not know what components are measured in the AKM. This study aims to develop a minimum ability assessment test instrument (AKM) for reading literacy and numeracy in the thematic learning of fifth grade elementary school based on the Program International Student Assessment (PISA) framework. The research method used is a research and development (R&amp;D) method with a 4D development model (Define, Design, Develop, and Disseminate). This research was carried out with a small-scale test at SDIT AkmalaSabila with 19 students and a large-scale test at SDN 6 Arjawinangun, Cirebon Regency with 47 students. In addition to developing the AKM test instrument based on the PISA framework, this research is also to assess the Eligibility and quality aspects of the AKM test instrument based on the PISA framework that was developed. Data and collection techniques in this study include questionnaires, tests, and documentation. The data were analyzed using several formulas of validity, reliability, and level of difficulty. Based on the results of the study, the average percentage of Eligibility of the AKM test instrument based on the PISA framework obtained from the validation of material, language and construction experts is 93.75% in the very valid category. The results of the quality of the AKM test instrument based on the PISA framework shown that there were 26 or 83.87% items categorized valid, 5 or 17% items categorized as invalid. The result of Cronbach's Alpha coefficient value of 0.941 so that the question has a very high reliable value. The results of the difficulty level index were 23 or 88.46% questions in the medium category and 3 or 11.54% questions in the easy category. Keywords: Test Instrument, AKM, literacy and numeracy, framework PISA.
Application of Gamification-Based Online Learning on Student Learning Outcomes During the Covid-19 Pandemic

The Covid-19 pandemic case had an impact on student teaching and learning activities. Teaching and learning activities that were originally face-to-face changed to online or through the internet network. One of the impacts of online learning is that students feel bored, so the value of student learning outcomes decreases. To overcome these problems gamification is applied to online learning. Gamification is a learning strategy that includes game elements in it. This study aims to see the effectiveness of gamification-based online learning on student learning outcomes. The method used in this research is quasi-experimental with a nonequivalent control group design. The results showed that there were differences between the post-test scores of the gamification-based online learning group and the regular online learning group. This is reinforced by the value of descriptive statistics, namely the mean value of the gamification-based online learning post-test is higher than the post-test value of ordinary online learning. Based on the results of the N-Gain test, the mean value for the N-Gain test in the gamification-based online learning group of 77,3095% is included in the effective category. It was concluded that gamification-based online learning is effective for improving student learning outcomes. Keywords: online learning, gamification, learning outcomes, effectiveness
The Effectiveness of Instagram for Enhancing Students Reading Comprehension to Students with High and Low Motivation (A Case Study at the 9th Grade of SMPK Santo Albertus Sampit, Kalimantan Tengah in the Academic Year of 2022/2023)

This research aims to analyse the Effectiveness Of Instagram For Enhancing Students Reading Comprehension to Students With High and Low Motivation (A Case Study at the 9th Grade of SMPK Santo Albertus Sampit, Kalimantan Tengah in the Academic Year of 2022/2023). This type of research is quantitative using an experimental study approach. This research was conducted at SMK Santo Albertus Sampit, Central Kalimantan, with a population of 9B and 9C graders with a total of 51 students. Then the population is divided into 2 classes, namely the experimental class and the control class as the research sample. The instrument used in this research is a questionnaire. From the tests carried out, it was found that the t test in this study showed that Instagram media was more effective than conventional media, as evidenced by the two-sided P value of 0.002 which is smaller than the value of α = 0.05 (0.002 &lt;0.05), whereas the calculated t value is greater than the t table value (3.225 &gt; 2.00), meaning that H0 is rejected and Ha is accepted or in other words there is a significant difference between the learning outcomes of students' reading comprehension in the control class and the experimental class. The difference in learning outcomes in the two classes is actually the impact of the treatment given to the two classes and shows which combination of media and learning models is more effective in students' reading comprehension. The average value of the control class in the posttest was 76.20 while the experimental class was 85.58. Calculation of the n-gain of both classes with the help of PSS.25 shows that the n-gain value (mean increase) of the experimental class is higher, namely 33.85 and the control class only gets a score of 21.80 on the n-gain of the exam. Based on the results of the calculation of the t-test and n-gain test, it is known that there is a difference in the average value of learning outcomes between the control class and the experimental class and an increase in the average value of the experimental class. those taught using Instagram media were higher than the control class taught using conventional media. Keywords: Effectiveness of Instagram, Enhancing Students, Reading Comprehension, High and Low Motivation
Automatic Correction of Human Translations

We introduce translation error correction (TEC), the task of automatically correcting human-generated translations. Imperfections in machine translations (MT) have long motivated systems for improving translations post-hoc with automatic post-editing. In contrast, little attention has been devoted to the problem of automatically correcting human translations, despite the intuition that humans make distinct errors that machines would be well-suited to assist with, from typos to inconsistencies in translation conventions. To investigate this, we build and release the Aced corpus with three TEC datasets. We show that human errors in TEC exhibit a more diverse range of errors and far fewer translation fluency errors than the MT errors in automatic post-editing datasets, suggesting the need for dedicated TEC models that are specialized to correct human errors. We show that pre-training instead on synthetic errors based on human errors improves TEC F-score by as much as 5.1 points. We conducted a human-in-the-loop user study with nine professional translation editors and found that the assistance of our TEC system led them to produce significantly higher quality revised translations.
MSDF: A General Open-Domain Multi-Skill Dialog Framework

Dialog systems have achieved significant progress and have been widely used in various scenarios. The previous researches mainly focused on designing dialog generation models in a single scenario, while comprehensive abilities are required to handle tasks under various scenarios in the real world. In this paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which can be applied in different dialog tasks (e.g. knowledge grounded dialog and persona based dialog). Specifically, we propose a transferable response generator pre-trained on diverse large-scale dialog corpora as the backbone of MSDF, consisting of BERT-based encoders and a GPT-based decoder. To select the response consistent with dialog history, we propose a consistency selector trained through negative sampling. Moreover, the flexible copy mechanism of external knowledge is also employed to enhance the utilization of multiform knowledge in various scenarios. We conduct experiments on knowledge grounded dialog, recommendation dialog, and persona based dialog tasks. The experimental results indicate that our MSDF outperforms the baseline models with a large margin. In the Multi-skill Dialog of 2021 Language and Intelligence Challenge, our general MSDF won the 3rd prize, which proves our MSDF is effective and competitive.
AutoML Two-Sample Test

Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of many sophisticated test procedures going beyond the standard supervised learning frameworks, whose usage can require specialized knowledge about two-sample testing. We use a simple test that takes the mean discrepancy of a witness function as the test statistic and prove that minimizing a squared loss leads to a witness with optimal testing power. This allows us to leverage recent advancements in AutoML. Without any user input about the problems at hand, and using the same method for all our experiments, our AutoML two-sample test achieves competitive performance on a diverse distribution shift benchmark as well as on challenging two-sample testing problems. We provide an implementation of the AutoML two-sample test in the Python package autotst.
An invitation to sequential Monte Carlo samplers

Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits.
The Complexity of Norm Synthesis and Revision

Norms have been widely proposed as a way of coordinating and controlling the activities of agents in a multi-agent system (MAS). A norm specifies the behaviour an agent should follow in order to achieve the objective of the MAS. However, designing norms to achieve a particular system objective can be difficult, particularly when there is no direct link between the language in which the system objective is stated and the language in which the norms can be expressed. In this paper, we consider the problem of synthesising a norm from traces of agent behaviour, where each trace is labelled with whether the behaviour satisfies the system objective. We show that the norm synthesis problem is NP-complete.
Presupposition projection as a scope phenomenon

The satisfaction theory of presupposition projection found in Heim 1983 has paved the way for a successful research tradition within dynamic semantics which has given rise to compositional analyses of a variety of projection behaviors. Since Geurts 1996, however, the promise of this research program has been called into question due to what Geurts dubs the “proviso problem”: satisfaction theory generates incorrect predictions in cases in which a presupposition ends up filtered which should not have been. I show that the satisfaction account of presupposition projection is nevertheless in good shape by revealing that the observations of Geurts are valid only under certain basic assumptions about how semantic composition works. To illustrate this, I present a satisfaction account of presupposition projection that incorporates a notion of scope-taking based on monads. The resulting composition scheme provides a setting in which the proviso problem does not arise, thus lending support to the scope theory of presupposition projection. EARLY ACCESS
Fuzzy Sugeno Method for Opinion Classification Regarding Policy of PPKM and Covid-19 Vaccination

The Indonesian government has implemented various interventions to overcome the impact of the Covid-19 pandemic, including those written in Minister of Home Affairs Instructions on PPKM (Community Activities Restrictions Enforcement) and Covid-19 vaccination policies. This policy are not at least reaping the pros and cons, so it is necessary to monitor public opinion to be able to provide solutions or become an evaluation of future policies. The aim of this study is to determine the polarity of public opinion regarding PPKM and Covid-19 vaccinations policies on Twitter, as well as to determine the implementation of FIS Sugeno in classifying textual data. There are several stages carried out, i.e. data collection, data pre-processing, data labeling, data weighting, identification of membership functions, determination of fuzzy sets, formation of a classification system, and evaluation of classification results. In this study, the performance of FIS Sugeno in classifying tweets was quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding the PPKM and Covid-19 vaccination policies tends to be balanced with 36.92% of tweets classified as a positive sentiments, 22.85% being negative sentiments, and another 40.23% belonging to neutral sentiments.
Perspectives on the First Amendment

Given the constitutional protection of free speech under the First Amendment, to what extent does the American public support that hate speech can be banned while still protecting free speech? The First Amendment states that “congress shall make no law… abridging the freedom of speech.” This project analyzes published data from the Cato Institute Free Speech and Tolerance Survey (2017) to determine the role that Americans' generation plays in their support of a hate speech ban. Individuals of various generations differ in their experiences with world events, politics and social norms. These differences can impact views on free speech. Preliminary analysis by the Cato Institute shows that younger generations are more likely to support that hate speech can be banned while still protecting free speech than older generations. Current scholarship lacks a depth of research in this area. As such, this project establishes relationships concerning views on hate speech that are a novel contribution to this field of study.
Multi-Lingual Sign language Detection System

Abstract: Sign language is the best possible communication facility for the hearing-impaired population as a result, its importance increases and research needs to be done for the systems successful implementation. This medium is a vital part of the communication process between individuals with hearing loss. In this paper, we present a framework for developing a word recognition system using a deep learning method where each method has its strength and limitations. Here, we review the various methods available in sign language recognition such as convolutional neural network (CNN) along with long short-term memory (LSTM), recurrent neural network (RNN), and hidden Markov model (HMM) understanding model behaviour and working. Creating a model which will read static as well as dynamic hand gestures and give output in written format.