Reviewerly/exHarmony huggingface dataset:
SISTEM INFORMASI ABSENSI RFID BERBASIS WEB MENGGUNAKAN ESP32 DI PT DHARMA SENTOSA MARINDO

In today's era, human daily activities cannot be separated from the use of technology. One of them is business activities. PT Dharma Sentosa Marindo is a company engaged in the shipyard industry. So far, the attendance administration records at PT Dharma Sentosa Marindo still use paper media which are then processed and stored on a computer manually. This makes the attendance process slower and less accurate, causing delays in generating attendance reports. This study aims to design an attendance information system at PT Dharma Sentosa Marindo to increase the speed and accuracy of recording attendance administration at PT Dharma Sentosa Marindo. The system design method uses the scrum method which uses the principles of agile development methodology. The results of system testing using the blackbox testing method show that the designed system has run smoothly and as expected
RANCANG BANGUN SISTEM E-ADMINISTRASI BERBASIS CODEIGNITER FRAMEWORK DI KP2A BATAM

This study aims to design and build an e-administration information system based on the CodeIgniter framework at KP2A Batam to solve administrative problems. Some of the administrative services currently being carried out by KP2A are registration of prospective students who wish to join KP2A, payment of monthly course fees, payment of book fees, photocopy fees, and expenses. The problem currently being faced by KP2A is in its administrative activities, starting from the admin recording the names of prospective students on paper, recording payment for books on paper, recording photocopy costs on paper, and recording expenses used to support teaching activities. everything is done by hand. The problem that often arises is one of them is the payment of fees that are not by the truth or a partial payment arises. From these problems, an e-administration information system was built that can make stored old and new data more organized and safer, prevent partial payment problems, and reduce errors in reporting. The method used is SDLC with the development of an agile scrum model. Research is expected to have a positive impact on readers, writers, and research objects.&#x0D; &#x0D; &#x0D;
PERANCANGAN AUGMENTED REALITY SEBAGAI PEMBELAJARAN ANATOMI MANUSIA BERBASIS ANDROID

Learning is a human effort in understanding basic knowledge. The learning process must meet the interactive, fun, and give students the ability to develop creativity. Interesting learning media must be applied. Human organ material is course that is not easy to understand, if you use books as a literature review. Human organ material is only limited to print media such as presentation, and books. This learning leads to a less interactive understanding, and is too monotonous, as well as learning becomes bored. Augmented reality is a medium that connects the virtual world with the real world. Augmented reality is used to facilitate learning by providing information virtually, not only used for the environment, but combining two-dimensional or three-dimensional virtual then projected into an object in real time. Augmented reality uses the android-based Marker Based Tracking method. Application creation using tools such as Unity, Vuforia, Blender. The system design method uses the System Development Life Cycle (SDLC) method or the waterfall method. The purpose of this study is the formation of an android-based augmented reality application as an interesting and more interactive learning medium for users
An integrated framework for classifying mammograms according to BIRADS scale and breast tissue density.

In this work, we present an integrated framework for classifying digital or digitized mammograms according to either BIRADS scale or breast density score. We propose unified data preparation procedure, a common training process that includes a Convolutional Neural Network approach and a simple inference tool aiming to ensure the robustness of out model and provide a more generalized solution. We utilize not only a private dataset for training and validation, but also the open dataset INBreast for the testing procedure. According to our results, our solution achieves; a) accuracy score 87% and F1 score 82% on Density classification, and b) accuracy score 85% and F1 score 80% on BIRADS classification.
Cross-Language Tourism News Retrieval System Using Google Translate API on SEBI Search Engine

Cross-Language Information Retrieval (CLIR) is responsible for retrieving information stored in a language different from the language of the query provided by the user. Some translation methods commonly used in CLIR are Dictionary, Parallel corpora, Comparable corpora, Machine translator, Ontology, and Transitive-based. The query must be translated to the target language, followed by preprocessing and calculating the similarity between the query and all documents in the corpus. The problem is the time and accuracy of query translation. Moreover, the queries are not written as complete sentences according to certain language rules. Stemming, for example, every language has its own method. Indonesian has basic words and affixes in the form of prefixes, suffixes, infixes, and confixes, while English only has suffixes. Stemming takes a long time in text processing. In the Indonesian search engine (SEBI), the provision of cross-language tourism news retrieval is realized using the Google Translate API, which translates the Query and all documents into English, Porter's stemming technique to convert each term to its general form, and cosine similarity to calculate similarity. This approach can deliver cross-language tourism news instantly while increasing the precision and efficiency of the SEBI search engine, although some improvements are needed to provide a more precise and efficient similarity computation.
Development of an Android-Based Cultural Heritage Map App

This study aims to (1) develop an Android-based cultural heritage map app that can provide the location of cultural heritages in the Special Region in Yogyakarta along with its descriptions, images, and videos; and (2) understand the quality of the app based on standard software quality testing ISO / IEC 25010 on functional suitability, performance efficiency, and compatibility. The development method utilized a waterfall software development model that consists of communication, planning, modeling, construction, and deployment. The results of this study are 1) an Android-based cultural heritage map app that can provide the location of cultural heritages in the Special Region of Yogyakarta along with its descriptions, pictures, and videos. and 2) the test results indicate the app meets the standards of ISO / IEC 25010 on the aspect of (1) functional suitability, entire functions of the app running 100%, (2) the compatibility of the app, compatible 100% of the co-existence, various operating systems, device types, and screen dimensions, (3) performance efficiency, the app successfully executed in 312 of the 321 test devices without any memory leak with the average of time behavior, CPU utilization, and memory utilization for Dalvik Virtual Machine are 0.022 seconds/thread, 10.71%, and 33.11 MB and the average of time behavior, CPU utilization, and memory utilization for Android Run Time are 0.020 seconds/thread, 9.9918%, and 154.582 MB.
Breaking Down Computer Networking Instructional Videos: Automatic Summarization with Video Attributes and Language Models

Instructional videos have become a popular tool for teaching complex topics in computer networking. However, these videos can often be lengthy and time-consuming, making it difficult for learners to obtain the key information they need. In this study, we propose an approach that leverages automatic summarization and language models to generate concise and informative summaries of instructional videos. To enhance the performance of the summarization algorithm, we also incorporate video attributes that provide contextual information about the video content. Using a dataset of computer networking tutorials, we evaluate the effectiveness of the proposed method and show that it significantly improves the quality of the video summaries generated. Our study highlights the potential of using language models in automatic summarization and suggests that incorporating video attributes can further enhance the performance of these models. These findings have important implications for the development of effective instructional videos in computer networking and can be extended to other domains as well.
Fake News Detection Using Naive Bayes Classifier: A Comparative Study

Machine learning is a subfield of artificial intelligence (AI) and computer science that utilizes data and algorithms to imitate how people learn, progressively improving its accuracy. Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights. Detecting fake news comes under a classification problem. Fake news is false or misleading information presented as news. The initial stage in classification is dataset collection, which is followed by preprocessing, feature selection, dataset training and testing, and finally executing the classifier. There is a large amount of written text in the news. This text is processed using NLP. NLP can perform an intelligent analysis of large amounts of plain written text and generate insights from it. It involves methods like data preprocessing and feature selection. Data pre-processing involves data cleaning, removing any incorrect, duplicate, or incomplete data within a dataset. Feature selection is done using the CountVectorizer and TF-IDF Vectorizer. Then comes dataset training and testing and the use of similar data for training and testing reduces the impact of data inconsistencies. After processing the model using the training set, the model is tested by making predictions against the test set. Then, to assess the performance of the classification model for the provided set of test data confusion matrix is used. The primary purpose is to use the Naive Bayes (NB) Classifier technique to generate two classification models one using CountVectorizer and other using TF-IDF Vectorizer and compare their accuracy.
Joint Model Pruning and Topology Construction for Accelerating Decentralized Machine Learning

Recently, mobile and embedded devices worldwide generate a massive amount of data at the network edge. To efficiently exploit the data from distributed devices, we concentrate on decentralized machine learning (DML), where the workers collaboratively train models under the peer-to-peer (P2P) setting. DML avoids the bottleneck of the parameter server (PS) by enabling the workers to exchange local models with their neighbors rather than the PS. However, DML still faces some key challenges, i.e., resource limitation, system heterogeneity, network dynamics and non-IID data. In this article, we design and implement MOTOR, an efficient DML mechanism that simultaneously addresses these challenges by applying model pruning and topology construction, thus accelerating DML. Specifically, MOTOR assigns different pruning ratios to heterogeneous workers. After model pruning, each worker will train and transmit a sub-model that fits its capabilities, reducing both computation and communication overhead. Besides, MOTOR dynamically constructs the network topology considering the time-varying network conditions and non-IID data distributions. We theoretically analyze the impact of pruning ratio and network topology on model training performance. Guided by the theoretical analysis, we develop a joint optimization algorithm for pruning ratio decision and topology construction to achieve the trade-off between resource overhead and training performance. We implement MOTOR on commercial devices and evaluate the performance with different DML tasks. Extensive experiments show that MOTOR achieves up to 4.2× speedup compared to the existing DML approaches.
Drivers of Student Technology Readiness in Using Cloud Accounting to Improve Student Performance

Cloud technology can make the learning process easy and done anywhere. However, in its use, some students feel anxious and prefer learning using offline software. This research targets students in Indonesia who study cloud accounting in computer accounting courses and introductory accounting. Researchers used the census method to get respondents, so a sample of 131 respondents was obtained. The incoming data is then tested for validity and reliability using PLS (partial least square) with the help of smartpls 3.0. After that, an analysis of technology readiness was carried out using technology readiness index (TRI), which consisted of optimism, innovativeness, insecurity, and discomfort. The results show that most students have medium technology readiness, with motivational factors such as optimism and innovativeness having a very high category. At the same time, the inhibiting factors in adopting technologies such as insecurity and discomfort are in the medium category. Most students also have the characteristics of explorers who like new technology, so cloud accounting is welcomed with great enthusiasm. However, based on the characteristics of male and female students, the technological readiness of male students is better than that of female students. One aspect that is lacking is the aspect of insecurity. Female students are worried when the assignments given do not reach their lecturers. So, cloud accounting service providers should provide an ecosystem that can facilitate all students. In addition, the findings of this study can also be helpful for lecturers to carry out learning by looking at the TRI from students so that learning can be more effective.
Vela Ramamurthi Puthinangalil ‘Eruthugattu’

Any creator sees events, grows and lives in his own soil. Therefore, in his mind, the land of his birth, the people who lived there, and the news he saw and heard there became the raw material for his work. Writing and reading provide concentration for typing. In this way, Vela Ramamurthy, who is a native of the region, is a creator of works based on Appanadu located in Kamudi area of Ramanathapuram district. The ideas recorded in his works are diverse. This article explores and explains his records about the heroic game called Erudugatu which is held in the area.
Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training

Feature attribution methods highlight the important input tokens as explanations to model predictions, which have been widely applied to deep neural networks towards trustworthy AI.However, recent works show that explanations provided by these methods face challenges of being faithful and robust.In this paper, we propose a method with Robustness improvement and Explanation Guided training towards more faithful EXplanations (REGEX) for text classification.First, we improve model robustness by input gradient regularization technique and virtual adversarial training.Secondly, we use salient ranking to mask noisy tokens and maximize the similarity between model attention and feature attribution, which can be seen as a self-training procedure without importing other external information.We conduct extensive experiments on six datasets with five attribution methods, and also evaluate the faithfulness in the out-of-domain setting.The results show that REGEX improves fidelity metrics of explanations in all settings and further achieves consistent gains based on two randomization tests.Moreover, we show that using highlight explanations produced by REGEX to train selectthen-predict models results in comparable task performance to the end-to-end method.
Identifying Slurs and Lexical Hate Speech via Light-Weight Dimension Projection in Embedding Space

The prevalence of hate speech on online platforms has become a pressing concern for society, leading to increased attention towards detecting hate speech. Prior work in this area has primarily focused on identifying hate speech at the utterance level that reflects the complex nature of hate speech. In this paper, we propose a targeted and efficient approach to identifying hate speech by detecting slurs at the lexical level using contextualized word embeddings. We hypothesize that slurs have a systematically different representation than their neutral counterparts, making them identifiable through existing methods for discovering semantic dimensions in word embeddings. The results demonstrate the effectiveness of our approach in predicting slurs, confirming linguistic theory that the meaning of slurs is stable across contexts. Our robust hate dimension approach for slur identification offers a promising solution to tackle a smaller yet crucial piece of the complex puzzle of hate speech detection.
Sentiment and Emotion Classification in Low-resource Settings

The popularity of sentiment and emotion analysis has lead to an explosion of datasets, approaches, and papers.However, these are often tested in optimal settings, where plentiful training and development data are available, and compared mainly with recent state-of-the-art models that have been similarly evaluated.In this paper, we instead present a systematic comparison of sentiment and emotion classification methods, ranging from rule-and dictionary-based methods to recently proposed few-shot and prompting methods with large language models.We test these methods indomain, out-of-domain, and in cross-lingual settings and find that in low-resource settings, rule-and dictionary-based methods perform as well or better than few-shot and prompting methods, especially for emotion classification.Zero-shot cross-lingual approaches, however, still outperform in-language dictionary induction.
cTBLS: Augmenting Large Language Models with Conversational Tables

Optimizing accuracy and performance while eliminating hallucinations of open-domain conversational large language models (LLMs) is an open research challenge.A particularly promising direction is to augment and ground LLMs with information from structured sources.This paper introduces Conversational Tables (cTBLS), a three-step architecture to retrieve and generate dialogue responses grounded on retrieved tabular information.cTBLS uses Transformer encoder embeddings for Dense Table Retrieval and obtains up to 125% relative improvement over the retriever in the previous state-of-the-art system on the HYRBIDIALOGUE dataset.cTBLS then uses a shared process between encoder and decoder models to perform a coarse+fine tabular knowledge (e.g., cell) ranking combined with a GPT-3.5 LLM response generator to yield a 2x relative improvement in ROUGE scores.Finally, human evaluators prefer cTBLs +80% of the time (coherency, fluency) and judge informativeness to be 4x better than the previous state-of-the-art.
Unsupervised Domain Adaptation using Lexical Transformations and Label Injection for Twitter Data

Domain adaptation is an important and widely studied problem in natural language processing. A large body of literature tries to solve this problem by adapting models trained on the source domain to the target domain. In this paper, we instead solve this problem from a dataset perspective. We modify the source domain dataset with simple lexical transformations to reduce the domain shift between the source dataset distribution and the target dataset distribution. We find that models trained on the transformed source domain dataset performs significantly better than zero-shot models. Using our proposed transformations to convert standard English to tweets, we reach an unsupervised part-of-speech (POS) tagging accuracy of 92.14% (from 81.54% zero shot accuracy), which is only slightly below the supervised performance of 94.45%. We also use our proposed transformations to synthetically generate tweets and augment the Twitter dataset to achieve state-of-the-art performance for POS tagging.
A Survey of Challenges and Methods in the Computational Modeling of Multi-Party Dialog

Advances in conversational AI systems, powered in particular by large language models, have facilitated rapid progress in understanding and generating dialog.Typically, task-oriented or open-domain dialog systems have been designed to work with two-party dialog, i.e., the exchange of utterances between a single user and a dialog system.However, modern dialog systems may be deployed in scenarios such as classrooms or meetings where conversational analysis of multiple speakers is required.This survey will present research around computational modeling of "multi-party dialog", outlining differences from two-party dialog, challenges and issues in working with multi-party dialog, and methods for representing multiparty dialog.We also provide an overview of dialog datasets created for the study of multiparty dialog, as well as tasks that are of interest in this domain.
Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models

We analyze sentiment analysis and toxicity detection models to detect the presence of explicit bias against people with disability (PWD).We employ the bias identification framework of Perturbation Sensitivity Analysis to examine conversations related to PWD on social media platforms, specifically Twitter and Reddit, in order to gain insight into how disability bias is disseminated in real-world social settings.We then create the Bias Identification Test in Sentiment (BITS) corpus to quantify explicit disability bias in any sentiment analysis and toxicity detection models.Our study utilizes BITS to uncover significant biases in four open AIaaS (AI as a Service) sentiment analysis tools, namely TextBlob, VADER, Google Cloud Natural Language API, DistilBERT and two toxicity detection models, namely two versions of Toxic-BERT.Our findings indicate that all of these models exhibit statistically significant explicit bias against PWD.
Analyzing Subjectivity Using a Transformer-Based Regressor Trained on Naïve Speakers’ Judgements

The problem of subjectivity detection is often approached as a preparatory binary task for sentiment analysis, despite the fact that theoretically subjectivity is often defined as a matter of degree. In this work, we approach subjectivity analysis as a regression task and test the efficiency of a transformer RoBERTa model in annotating subjectivity of online news, including news from social media, based on a small subset of human-labeled training data. The results of experiments comparing our model to an existing rule-based subjectivity regressor and a state-of-the-art binary classifier reveal that: 1) our model highly correlates with the human subjectivity ratings and outperforms the widely used rule-based “pattern” subjectivity regressor (De Smedt and Daelemans, 2012); 2) our model performs well as a binary classifier and generalizes to the benchmark subjectivity dataset (Pang and Lee, 2004); 3) in contrast, state-of-the-art classifiers trained on the benchmark dataset show catastrophic performance on our human-labeled data. The results bring to light the issues of the gold standard subjectivity dataset, and the models trained on it, which seem to distinguish between the origin/style of the texts rather than subjectivity as perceived by human English speakers.
Linguistic Properties of Truthful Response

We investigate the phenomenon of an LLM's untruthful response using a large set of 220 handcrafted linguistic features.We focus on GPT-3 models and find that the linguistic profiles of responses are similar across model sizes.That is, how varying-sized LLMs respond to given prompts stays similar on the linguistic properties level.We expand upon this finding by training support vector machines that rely only upon the stylistic components of model responses to classify the truthfulness of statements.Though the dataset size limits our current findings, we show the possibility that truthfulness detection is possible without evaluating the content itself.But at the same time, the limited scope of our experiments must be taken into account in interpreting the results.