Reviewerly/exHarmony huggingface dataset:
Prediction of Financial Crime Using Machine Learning

The purpose of data analytics is to uncover previously unknown patterns and make use of such patterns to help in making educated decisions across a wide range of contexts. Because of advances in modern technology and the fact that credit cards have become an easy target for fraudulent activity, the incidence of credit card fraud has considerably increased in recent years. Credit card fraud is a significant issue in the industry of financial services, and it results in annual losses of billions of dollars. The development of a fraud detection algorithm is a difficult endeavor due to the paucity of real-world transaction datasets that are available due to confidentiality concerns and the very unbalanced nature of the datasets that are publicly available. Use a dataset from the real world in conjunction with a variety of supervised machine learning algorithms to identify potentially fraudulent credit card transactions. In addition, make use of these techniques to create a super classifier through the use of ensemble learning methods. Determine which variables are the most significant and could perhaps lead to a higher level of accuracy in the identification of fraudulent credit card transactions. In addition, we evaluate and discuss the performance of a number of other supervised machine learning algorithms that are currently available in the literature in contrast to the super classifier that can be implemented.
RANCANG BANGUN SISTEM INFORMASI KEPEGAWAIAN BERBASIS WEB PADA PT SRI INDAH MANDIRI

Today's technology has developed rapidly. This can be seen from the many uses of technology in everyday human life. PT Sri Indah Mandiri is a company that has been established since 1997 which is engaged in manufacturing to provide building materials. So far, the administrative recording process for the presence of PT Sri Indah Mandiri still relies on the application of Ms. Excel and printed documents resulting in an increasing number of accumulations of attendance data and searching for data takes quite a long time. In addition, the calculation of overtime is still done manually, so it often causes errors in the process of calculating employee overtime. This study aims to design a staffing information system at PT Sri Indah Mandiri to improve and facilitate the process of recording attendance at PT Sri Indah Mandiri. The system development method used is the Agile Scrum SDLC method. The results of the system design show that the system improves and facilitates the process of recording attendance so that it can make PT Sri Indah Mandiri operate more effectively and efficiently in its business activities.
PERANCANGAN APLIKASI GAME EDUKASI TENTANG MITOLOGI INDONESIA BERBASIS ANDROID

The rapid development of technology has significantly impacted various aspects of human life, including education and entertainment. One emerging trend is the utilization of technology to create engaging and interactive educational games. With technological advancements, educational games have become effective tools to teach various concepts and skills to the younger generation. However, in the era of globalization, the role of mythology in the lives of the younger generation has been overshadowed by foreign cultures' influence and technological advancements. By combining technology, elements of mythology, and educational aspects, a game based on Indonesian mythology becomes an innovative and attractive solution to ignite the interest and awareness of the younger generation towards their cultural heritage and national history. In this research will developed a technology-based educational game with a focus on Indonesian mythology using Unity, Adobe Illustrator and Visual Studio. The game was designed to present information about Indonesian mythology in an engaging and interactive manner, captivating the interest of the younger generation in learning and getting acquainted with their cultural heritage.
PREME: Preference-based Meeting Exploration through an Interactive Questionnaire

The recent increase in the volume of online meetings necessitates automated tools for organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it. In this work, we propose a novel end-to-end framework for generating interactive questionnaires for preference-based meeting exploration. As a result, users are supplied with a list of suggested questions reflecting their preferences. Since the task is new, we introduce an automatic evaluation strategy by measuring how much the generated questions via questionnaire are answerable to ensure factual correctness and covers the source meeting for the depth of possible exploration.
VarDial in the Wild: Industrial Applications of LID Systems for Closely-Related Language Varieties

This report describes first an industrial use case for identifying closely related languages, e.g.dialects, namely the detection of languages of movie subtitle documents. We then presenta 2-stage architecture that is able to detect macrolanguages in the first stage and languagevariants in the second. Using our architecture, we participated in the DSL-TL Shared Task of the VarDial 2023 workshop. We describe the results of our experiments. In the first experiment we report an accuracy of 97.8% on a set of 460 subtitle files. In our second experimentwe used DSL-TL data and achieve a macroaverage F1 of 76% for the binary task, and 54% for the three-way task in the dev set. In the open track, we augment the data with named entities retrieved from Wikidata and achieve minor increases of about 1% for both tracks.
MUTANT: A Multi-sentential Code-mixed Hinglish Dataset

The multi-sentential long sequence textual data unfolds several interesting research directions pertaining to natural language processing and generation. Though we observe several high-quality long-sequence datasets for English and other monolingual languages, there is no significant effort in building such resources for code-mixed languages such as Hinglish (code-mixing of Hindi-English). In this paper, we propose a novel task of identifying multi-sentential code-mixed text (MCT) from multilingual articles. As a use case, we leverage multilingual articles from two different data sources and build a first-of-its-kind multi-sentential code-mixed Hinglish dataset i.e., MUTANT. We propose a token-level language-aware pipeline and extend the existing metrics measuring the degree of code-mixing to a multi-sentential framework and automatically identify MCT in the multilingual articles. The MUTANT dataset comprises 67k articles with 85k identified Hinglish MCTs. To facilitate future research directions, we will make the dataset and the code publicly available upon publication.
Evaluating the Diversity, Equity, and Inclusion of NLP Technology: A Case Study for Indian Languages

In order for NLP technology to be widely applicable, fair, and useful, it needs to serve a diverse set of speakers across the world’s languages, be equitable, i.e., not unduly biased towards any particular language, and be inclusive of all users, particularly in low-resource settings where compute constraints are common. In this paper, we propose an evaluation paradigm that assesses NLP technologies across all three dimensions. While diversity and inclusion have received attention in recent literature, equity is currently unexplored. We propose to address this gap using the Gini coefficient, a well-established metric used for estimating societal wealth inequality. Using our paradigm, we highlight the distressed state of current technologies for Indian (IN) languages (a linguistically large and diverse set, with a varied speaker population), across all three dimensions. To improve upon these metrics, we demonstrate the importance of region-specific choices in model building and dataset creation, and more importantly, propose a novel, generalisable approach to optimal resource allocation during fine-tuning. Finally, we discuss steps to mitigate these biases and encourage the community to employ multi-faceted evaluation when building linguistically diverse and equitable technologies.
PePe: Personalized Post-editing Model utilizing User-generated Post-edits

Incorporating personal preference is crucial in advanced machine translation tasks. Despite the recent advancement of machine translation, it remains a demanding task to properly reflect personal style. In this paper, we introduce a personalized automatic post-editing framework to address this challenge, which effectively generates sentences considering distinct personal behaviors. To build this framework, we first collect post-editing data that connotes the user preference from a live machine translation system. Specifically, real-world users enter source sentences for translation and edit the machine-translated outputs according to the user’s preferred style. We then propose a model that combines a discriminator module and user-specific parameters on the APE framework. Experimental results show that the proposed method outperforms other baseline models on four different metrics (i.e., BLEU, TER, YiSi-1, and human evaluation).
Know your audience: specializing grounded language models with listener subtraction

Effective communication requires adapting to the idiosyncrasies of each communicative context—such as the common ground shared with each partner. Humans demonstrate this ability to specialize to their audience in many contexts, such as the popular game Dixit. We take inspiration from Dixit to formulate a multi-agent image reference game where a (trained) speaker model is rewarded for describing a target image such that one (pretrained) listener model can correctly identify it among distractors, but another listener cannot. To adapt, the speaker must exploit differences in the knowledge it shares with the different listeners. We show that finetuning an attention-based adapter between a CLIP vision encoder and a large language model in this contrastive, multi-agent setting gives rise to context-dependent natural language specialization from rewards only, without direct supervision. Through controlled experiments, we show that training a speaker with two listeners that perceive differently, using our method, allows the speaker to adapt to the idiosyncracies of the listeners. Furthermore, we show zero-shot transfer of the specialization to real-world data. Our experiments demonstrate a method for specializing grounded language models without direct supervision and highlight the interesting research challenges posed by complex multi-agent communication.
Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarks

Searching troves of videos with textual descriptions is a core multimodal retrieval task. Owing to the lack of a purpose-built dataset for text-to-video retrieval, video captioning datasets have been re-purposed to evaluate models by (1) treating captions as positive matches to their respective videos and (2) assuming all other videos to be negatives. However, this methodology leads to a fundamental flaw during evaluation: since captions are marked as relevant only to their original video, many alternate videos also match the caption, which introduces false-negative caption-video pairs. We show that when these false negatives are corrected, a recent state-of-the-art model gains 25% recall points—a difference that threatens the validity of the benchmark itself. To diagnose and mitigate this issue, we annotate and release 683K additional caption-video pairs. Using these, we recompute effectiveness scores for three models on two standard benchmarks (MSR-VTT and MSVD). We find that (1) the recomputed metrics are up to 25% recall points higher for the best models, (2) these benchmarks are nearing saturation for Recall@10, (3) caption length (generality) is related to the number of positives, and (4) annotation costs can be mitigated through sampling. We recommend retiring these benchmarks in their current form, and we make recommendations for future text-to-video retrieval benchmarks.
An Effective Approach for Informational and Lexical Bias Detection

In this paper we present a thorough investigation of automatic bias recognition on BASIL, a dataset of political news which has been annotated with different kinds of biases. We begin by unveiling several inconsistencies in prior work using this dataset, showing that most approaches focus only on certain task formulations while ignoring others, and also failing to report important evaluation details. We provide a comprehensive categorization of these approaches, as well as a more uniform and clear set of evaluation metrics. We argue about the importance of the missing formulations and also propose the novel task of simultaneously detecting different kinds of biases in news. In our work, we tackle bias on six different BASIL classification tasks in a unified manner. Eventually, we introduce a simple yet effective approach based on data augmentation and preprocessing which is generic and works very well across models and task formulations, allowing us to obtain state-of-the-art results. We also perform ablation studies on some tasks to quantify the strength of data augmentation and preprocessing, and find that they correlate positively on all bias tasks.
A Novel Approach for Exam E-assessment Utilizing Image Processing

There is a brand-new feature called Exam (Infinity Exam) that supports paper-based exams and speeds up the entire process while maintaining all of their beneficial qualities and minimizing their drawbacks, notably in higher education. The method is very different from those employed in the earlier 10+ years, which were implemented in a way that prevented them from replicating and supplanting the conventional paper-based examination format. The article's core relies on the image processing flow, which is the most crucial component of the software. Multiple Choice Questions (MCQ) have been a more common method of testing someone's knowledge over time. The use of multiple choice questions in exams is becoming more widespread in the education sector (including in schools and colleges). It is employed even when conducting interviews. The current scenario involves either manually correcting the test or using OMR technology. Having OMR at all times in real time is rather challenging, and manually correcting it takes a lot of effort and could result in a mistake. We address this issue by applying a digital image processing technique in our proposed system to correct the response using multiple-choice questions written in Python. Here, we are processing data using Open-Source Computer Vision Library (OpenCV).
The role of Reinforcement Learning in software testing

Software testing is applied to validate the behavior of the software system and identify flaws and bugs. Different machine learning technique types such as supervised and unsupervised learning were utilized in software testing. However, for some complex software testing scenarios, neither supervised nor unsupervised machine learning techniques were adequate. As such, researchers applied Reinforcement Learning (RL) techniques in some cases. However, a systematic overview of the state-of-the-art on the role of reinforcement learning in software testing is lacking. The objective of this study is to determine how and to what extent RL was used in software testing. In this study, a Systematic Literature Review (SLR) was conducted on the use of RL in software testing, and 40 primary studies were investigated. This study highlights different software testing types to which RL has been applied, commonly used RL algorithms and architecture for learning, challenges faced, advantages and disadvantages of using RL, and the performance comparison of RL-based models against other techniques. RL has been widely used in software testing but has almost narrowed to two applications. There is a shortage of papers using advanced RL techniques in addition to multi-agent RL. Several challenges were presented in this study.
Entitlement Racism on YouTube: White injury—the licence to Humiliate Roma migrants in the UK

Hate speech monitoring has become a challenging task for social media platforms. While efforts have been made to combat racism and other forms of hate speech, marginalised communities, such as the Roma are frequent targets of intense discrimination and online racist abuse. This article examines manifestations of Romaphobia, also known as anti-Roma racism, on YouTube in the context of 2016 UK Referendum on EU membership, when Roma along with other Eastern European migrants became demonized in the right-wing narratives of Brexit supporters. Drawing on Thematic Analysis and Critical Discourse Analysis, the article provides an in-depth account of how Romaphobia is discursively constructed in the commentaries of those watching YouTube documentaries about Eastern European migrants. In addition to drawing attention to various forms of overt and covert racism that fly under the radar in the content moderation process, the analysis shows that ‘entitlement racism’ is gradually becoming normalised in social media, with anti-Roma racism acquiring alarming levels of verbal violence which needs immediate attention.
AN ANALYSIS OF DISCOVERY LEARNING STRATEGY IN ENGLISH LEARNING PROCESS AT EIGHTH GRADE ON SMP NEGERI 2 HILIDUHO IN 2022/2023

Discovery Learning Strategy is a strategy used by English teacher in English learning process. Discovery Learning Strategy can increase the students' activeness in English learning process. The purpose of this study is to find out how the implementation of Discovery Learning Strategy in the English learning process and secondly to analyse how students' English learning result in the implementation of Discovery Learning Strategy in English learning process. This research uses a descriptive qualitative approach. The research instruments used were lesson plans, observation sheets, and field notes. The results showed that in the learning process using the Discovery Learning Strategy, the English teacher had used the lesson plan as a guideline in learning. In the lesson plan, there are procedures carried out by the English teacher. The implementation of Discovery Learning Strategy in greeting card learning has been going well, it is proven that when English teacher do the learning process by using Discovery Learning Strategy, the students look active in participating in learning, especially in greeting card material. The implementation of Discovery Learning Strategies in class VIII-B had an impact on the student learning result. From the results of tests conducted by English teacher in the class that the implementation of Discovery Learning Strategies has a positive impact on improving the student learning results. From the test results that had been given many students who answer correctly and reach the MCC.
IMPROVING THE STUDENTS’ READING COMPREHENSION THROUGH LISTEN-READ-DISCUSS (LRD) STRATEGY AT THE EIGHTH GRADE OF SMP NEGERI 1 LAHEWA IN 2022/2023

Reading is the process of getting ideas from texts. Reading comprehension meant understanding what had been read and it was an active thinking process that depends. There are several problems faced by students in reading comprehension at the Eighth Grade of SMP Negeri 1 Lahewa, one of which was have problem in comprehending skill in their reading. Therefore, to overcome this, researcher conducted research using the Listen-Read-Discuss strategy. The purpose of the research is to Improving the Students’ Reading Comprehension through Listen-Read-Discussion (LRD) strategy at the Eighth Grade of SMP Negeri 1 Lahewa in 2022/2023. The results of the study were seen from the average score of students from the test evaluation sheet. There are 3 students (10%) included in the failed level, 3 students (10%) included in the less level, 11 students (36%) included in the sufficient level, 4 students (13%) included in good category and 9 students (30%) are included in the very good category. In Cycle II students' abilities increased. There are no students who are categorized as failed and lacking. The highest score is 100 and the lowest score is 75. There is 1 student (3%) "enough level". 4 students (13%) at "good level", and 25 students (83%) at "very good level". From the results of the study it can be concluded that the Listen-Read-Discuss Strategy improves reading comprehension at the Eighth Grade of SMP Negeri 1 Lahewa in 2022/2023.
INCREASING THE STUDENT’S VOCABULARY MASTERY THROUGH HOW SPECIAL AM I GAME OF THE TENTH GRADE SMK NEGERI 1 GUNUNGSITOLI IN 2022/2023

Vocabulary is the most important component of the overall communicative competence that enables students to convey information, ideas or notions to others in such written and oral form. Vocabulary mastery is the ability to use the words in conducting the communication and understanding the meaning of the words or phrases in English. The research was conducted by using Classroom Action Research (CAR) design. This research was applied into two cycles. Each cycle consisted of planning, action, observation, and reflection. In the second meeting of cycle II, the researcher obtained the improvement of students, who achieved the Minimum Competence Criterion (70). There were 14 (53%) students who got score among 85-100 classified as “Very Good Criteria”, 12 (42%) students got score among 75-84 classified as “Good Criteria”. Based on the result above the researcher concludes that How Special Am I Game could be done in teaching vocabulary and increasing the students’ ability in mastering vocabulary.
THE DESCRIPTION OF TEACHER’S THINK ALOUD STRATEGY IN TEACHING READING COMPREHENSION AT TENTH GRADE OF SMK NEGERI 1 SITOLU ORI IN 2022/2023

The aim of research is to describe the using of teacher’s Think Aloud Strategy in Teaching Reading Comprehension and to describe the advantages and disadvantages of Think Aloud Strategy in Teaching Reading Comprehension for students. In conducting the research, the researcher used qualitative research. The result showed that the implementation of Think Aloud Strategy by teacher adapted from theory of Bahri, et al. (2018). There were 6 activities that have been done and 1 activity that has not been done by teacher. The advantage of using Think Aloud Strategy in teaching reading comprehension were students more quickly find main ideas from text, improves students' way of thinking, students are active in learning, and students get new vocabulary. The disadvantage of Think Aloud Strategy were not all students could follow this strategy especially in giving opinion and students whose level of understanding is lack need the long time to answer questions.
INCREASING THE STUDENTS’ VOCABULARY MASTERY THROUGH JEOPARDY GAME AT THE EIGHTH GRADE OF SMP NEGERI 1 GUNUNGSITOLI BARAT IN 2022/2023

Vocabulary mastery is the ability to use the words in conducting the communication and understanding the meaning of the words or phrases in English. The purpose of the research is to Increase the Students’ Vocabulary Mastery Through Jeopardy Game at the Eighth Grade of SMP Negeri 1 Gunungsitoli Barat which the total numbers of the students was 26 persons. The research was conducted by using Classroom Action Research (CAR) design. This research was applied into two cycles. Each cycle consisted of planning, action, observation, and reflection. The instruments used by the researcher to collect the data were observation sheet, evaluation sheet and field note. From the result of each Cycle, the researcher finds that the students get an improvement in their vocabulary through Jeopardy Game. At the second meeting of cycle II the researcher obtained an increase in students, that students overall reached MCC (70).