Reviewerly/exHarmony huggingface dataset:
Teaching Indonesian Language for Foreign Speakers through Video Conferencing during Pandemic: A Case Study

The Covid-19 pandemic has shifted all face-to-face learning in Indonesia to online learning. The shift has also happened in learning Indonesian for Foreign Speakers (BIPA). Online learning of BIPA, as a second language, made many teachers choose to use video conferencing. This study aimed to describe and explain the use of video conferencing, as well as the obstacles and solutions to BIPA learning at the Alam Bahasa Institute Yogyakarta during the Covid-19 pandemic. This study was qualitative research that employed a case study method at the Alam Bahasa Institute Yogyakarta. The research data were collected using documentation, interview, and observation. The data were validated using the method and source triangulation. The data were then presented using an interactive method. The results of this study revealed that students and teachers at Alam Bahasa Institute Yogyakarta used video conferencing software in conducting BIPA learning, there was an increased number of students using video conferencing, there was an expansion of the video conferencing software being used, and there were particular features of video conferencing software needed in the online learning of Indonesian for Foreign Speakers (BIPA). Meanwhile, the obstacles experienced by the students and teachers at Alam Bahasa Institute Yogyakarta were poor internet connection, power outages, and boredom. The offered solutions were to ensure the stability of the internet connection, reschedule classes affected by power outages, and provide rest time amid learning hours.
TopWORDS-Seg: Simultaneous Text Segmentation and Word Discovery for Open-Domain Chinese Texts via Bayesian Inference

Processing open-domain Chinese texts has been a critical bottleneck in computational linguistics for decades, partially because text segmentation and word discovery often entangle with each other in this challenging scenario.No existing methods yet can achieve effective text segmentation and word discovery simultaneously in open domain.This study fills in this gap by proposing a novel method called TopWORDS-Seg based on Bayesian inference, which enjoys robust performance and transparent interpretation when no training corpus and domain vocabulary are available.Advantages of TopWORDS-Seg are demonstrated by a series of experimental studies.
COVIUAM: A mobile app to get information about COVID-19 cases

The COVID-19 pandemic took the world by surprise, its rapid spread and its death rate caused governments to make drastic decisions such as closing borders, establishing curfews, closing businesses, etc. in order to break the chains of infections. In many countries mobile apps were developed to have information on possible contagions and prevent their spread. This paper describes COVIUAM, a mobile app that collects information on suspected or confirmed cases of COVID-19 in members of the Metropolitan Autonomous University. Through the data collected by COVIUAM app, patterns can be identified in the information, for example in symptomatology data. The article highlights the design and architecture of COVIUAM app and presents two evaluations, one quantitative and one qualitative of the information collected and the use of the application.
Perbandingan Algoritma Random Forest Classifier, Support Vector Machine dan Logistic Regression Clasifier Pada Masalah High Dimension (Studi Kasus: Klasifikasi Fake News)

Fake news is false information that looks like it is true. News can also be said as a political weapon whose truth cannot be accounted for which is spread intentionally to achieve a certain goal. Classification of news texts requires calculating a method for each word in the document. Each word processed per document means that the number of data dimensions is equal to the number of words. The more the number of words in a document, the more the number of dimensions in each data (high dimension). The large number of dimensions (high dimension), causes the model-making process (training) to be long and the shortcomings are also clearly visible in seeing the similarity of documents (document similarity). The dataset used in this study amounted to 20000 and 17 attributes. The method used in this study uses a Random Forest Classifier (RFC), Support Vector Machine (SVM) and Logistic Regression (LR) with high dimensions and the results of this study are to obtain a comparison of the accuracy values for each method used
Analysis of Earthquake Activity in Indonesia by Clustering Method

Indonesia is an area where three large tectonic plates meet, namely the Indo-Australian, Eurasian and Pacific plates, so that Indonesia is included in the earthquake-prone category, with 11,660 earthquake vibrations identified in the Meteorology, Climatology and Geophysics Agency (BMKG) database in 2019 The purpose of this study is to develop a classification of the distribution of earthquakes in Indonesia in 2019 based on the values of magnitude, depth, and position. This research was conducted by using the clustering method based on the K-means algorithm and the DBSCAN algorithm as a comparison. The results of the clustering show that the earthquake data analysis using the K-Means algorithm is superior with a silhouette index value of 0.837, while the DBSCAN algorithm has a silhouette index value of 0.730.
Measuring Contact Pressures of phonating Vocal Fold using a Pressure-Sensing-Matrix during Hemi-Larynx Experiments

During phonation, the vocal folds execute a complex movement. Related to the subglottal pressure and tension of the vocal folds themselves, this movement may differ, furthermore depending on the kind of phonation a collision between both vocal folds may occur. If the vocal folds are overstrained, this can cause lesions. To gain knowledge about the vocal folds movements and the way of their collisions, a high-speed evaluation unit for a pressure-mapping sensor was developed. The sensor brings along a local resolution of 27.6 measure points per square centimeter and with the developed unit an adjustable framerate up to 1 kHz can be realized. Due to a sensor thickness of about 0.2 mm, an integration into hemi-larynx flow experiments is quite simple without vigorous changes on the setup. Because of an unrestricted field of view it is possible to record the movements with a high-speed camera. These recordings will be correlated with contact pressures in the post processing. During these experiments, different flowrates of the excitation air.
Strategi Meningkatkan Kemampuan Berbahasa Inggris

Language has been the target of studies by experts, scholars, researchers and writers in the field of language to reveal various things related to it. One issue that has often been under the highlight is language skills. As widely known, language skills are determined by two general skills, namely receptive and productive skills. Receptive skills consist of listening and reading, while productive skills consist of speaking and writing. Unfortunately, not all of the suggested findings can help foreign language learners to master it completely. Therefore, this study published in this report is the result of the implementation of Community Service activities carried out during the Covid-19 pandemic by using Zoom. Based on the results of this community service activity, this report recommends several strategies for improving language skills. This study begins with reviewing existing theories provided by experts and continued by exploring them through exploratory qualitative studies. The data in this study were collected through a literature study of language theories and language skills and strategies to improve them. The data were analyzed by sorting out theories about appropriate strategies to improve language skills separately and to improve all language skills. The result of the study shows that productive and receptive skills are better improved through routine and repetitive exercises, including communicating with ourself and others. Listening to music is an effective way to improve listening skills. Learners must be able to find a learning stimulus through the use of various electronic and online media as well. To strengthen this, teachers must combine various strategies in teaching one language skill so that students are able understand learning easily.&#x0D;
Self-supervised Semantic Segmentation Grounded in Visual Concepts

Unsupervised semantic segmentation requires assigning a label to every pixel without any human annotations. Despite recent advances in self-supervised representation learning for individual images, unsupervised semantic segmentation with pixel-level representations is still a challenging task and remains underexplored. In this work, we propose a self-supervised pixel representation learning method for semantic segmentation by using visual concepts (i.e., groups of pixels with semantic meanings, such as parts, objects, and scenes) extracted from images. To guide self-supervised learning, we leverage three types of relationships between pixels and concepts, including the relationships between pixels and local concepts, local and global concepts, as well as the co-occurrence of concepts. We evaluate the learned pixel embeddings and visual concepts on three datasets, including PASCAL VOC 2012, COCO 2017, and DAVIS 2017. Our results show that the proposed method gains consistent and substantial improvements over recent unsupervised semantic segmentation approaches, and also demonstrate that visual concepts can reveal insights into image datasets.
Ambiguity-Induced Contrastive Learning for Instance-Dependent Partial Label Learning

Partial label learning (PLL) learns from a typical weak supervision, where each training instance is labeled with a set of ambiguous candidate labels (CLs) instead of its exact ground-truth label. Most existing PLL works directly eliminate, rather than exploiting the label ambiguity, since they explicitly or implicitly assume that incorrect CLs are noise independent of the instance. While a more practical setting in the wild should be instance-dependent, namely, the CLs depend on both the true label and the instance itself, such that each CL may describe the instance from some sensory channel, thereby providing some noisy but really valid information about the instance. In this paper, we leverage such additional information acquired from the ambiguity and propose AmBiguity-induced contrastive LEarning (ABLE) under the framework of contrastive learning. Specifically, for each CL of an anchor, we select a group of samples currently predicted as that class as ambiguity-induced positives, based on which we synchronously learn a representor (RP) that minimizes the weighted sum of contrastive losses of all groups and a classifier (CS) that minimizes a classification loss. Although they are circularly dependent: RP requires the ambiguity-induced positives on-the-fly induced by CS, and CS needs the first half of RP as the representation extractor, ABLE still enables RP and CS to be trained simultaneously within a coherent framework. Experiments on benchmark datasets demonstrate its substantial improvements over state-of-the-art methods for learning from the instance-dependent partially labeled data.
Individual Fairness Guarantees for Neural Networks

We consider the problem of certifying the individual fairness (IF) of feed-forward neural networks (NNs). In particular, we work with the epsilon-delta-IF formulation, which, given a NN and a similarity metric learnt from data, requires that the output difference between any pair of epsilon-similar individuals is bounded by a maximum decision tolerance delta &gt;= 0. Working with a range of metrics, including the Mahalanobis distance, we propose a method to overapproximate the resulting optimisation problem using piecewise-linear functions to lower and upper bound the NN's non-linearities globally over the input space. We encode this computation as the solution of a Mixed-Integer Linear Programming problem and demonstrate that it can be used to compute IF guarantees on four datasets widely used for fairness benchmarking. We show how this formulation can be used to encourage models' fairness at training time by modifying the NN loss, and empirically confirm our approach yields NNs that are orders of magnitude fairer than state-of-the-art methods.
Multimodalities Element In The Cartoon Work Through Linguistic Modes And Visual Modes

In cartoons, meanings and messages are generated either through two semiotic modes that are verbal and visual or solely through visual modes. Interaction between language and cartoon images is often regarded as a straightforward and easy-to-process way to convey a message. This study aims to show that the meaning and cartoon messages are not always easy to understand, therefore readers should observe the language and visual information of each cartoon. This study is aimed at identifying the linguistic modes and the visual modes in the cartoons to convey the meaning and the explicit message or implied message. For this purpose, two theories are adopted; Systemic Functional Linguistics Theory by Halliday (1994) to analyze linguistic modes and multimodal approach by Kress and Van Leeuwen (1996, 2006) to analyze visual modes or images in cartoons. The research data was obtained from Usik-Usik cartoon book (Cynical Collection of the World of Education). The findings indicate that language text is a linguistic modes that acts as an effective multimodal element and complement the visual modes in the cartoon work. Furthermore, the findings of the study also show that the role of linguistic modes is to provide the context in the cartoon work as additional information so that the reader can understand the messages to be communicated more clearly.
PENGEMBANGAN LEMBAR KERJA PESERTA DIDIK ELEKTRONIK (E-LKPD) PEMBELAJARAN PROYEK BERBASIS LITERASI SAINS PADA MATERI PENCEMARAN LINGKUNGAN

The aim of this research was to determine the feasibility of the Electronic Student Worksheet (e-LKPD) based on science literacy for project learning on environmental pollution. This research employed a research and development (R&amp;D) form which refers to the stages of development according to Borg &amp; Gall. The science literacy-based e-LKPD was tested to 9 students of SMPN 3 Pontianak in the initial trial. In the main trial, the e-LKPD was tested to 36 students from three schools which are SMPN 2 Pontianak, SMPN 3 Pontianak and SMPN 4 Pontianak. The instruments for data collection were the feasibility assessment sheet and students’ response questionnaire. The result of data analysis showed that e-LKPD in terms of content, presentation, language and media with 96.8%; 98.2%; 94.4%; and 88.8% respectively. All of these percentages are categorized as very high criteria. The averaged percentage of students’ response on the initial trial and the main trial are 81.6% and 83.9% which categorized as very high criteria. Therefore, e-LKPD is feasible to be used in learning projects based on science literacy on environment pollution topic.Keywords: Science literacy, project based learning.
Entanglement: cybercrime connections of a public forum population

Many activities related to cybercrime operations do not require much secrecy, such as developing websites or translating texts. This research provides indications that many users of a popular public internet marketing forum have connections to cybercrime. It does so by investigating the involvement in cybercrime of a population of users interested in internet marketing, both at a micro and macro scale. The research starts with a case study of three users confirmed to be involved in cybercrime and their use of the public forum where users share information about online advertising. It provides a first glimpse that some business with cybercrime connection is being conducted in the clear. The study then pans out to investigate the forum population's ties with cybercrime by finding crossover users, who are users from the public forum who also comment on cybercrime forums. The cybercrime forums on which they discuss are analyzed and crossover users' strength of participation is reported. Also, to assess if they represent a sub-group of the forum population, their posting behavior on the public forum is compared with that of non-crossover users. This blend of analyses shows that (i) a minimum of 7.2% of the public forum population are crossover users that have ties with cybercrime forums; (ii) their participation in cybercrime forums is limited; and (iii) their posting behavior is relatively indistinguishable from that of non-crossover users. This is the first study to formally quantify how users of an internet marketing public forum, a space for informal exchanges, have ties to cybercrime activities. We conclude that crossover users are a substantial part of the population in the public forum, and, even though they have thus far been overlooked, their aggregated effect in the ecosystem must be considered.
A novel underwater color correction method based on underwater imaging model and generative adversarial network

Underwater vision has played an increasingly important role in ocean research, ocean military, and underwater fishing. The raw underwater images suffer from color distortion and poor image quality, which make underwater vision more challengeable than open air vision. To address the mentioned problems, a novel model for underwater image color correction is proposed in this paper. Firstly, a synthetic underwater image generating network is presented to overcome the lack of effective underwater training data. Specifically, it combines a background color prior model to generate synthetic underwater images via in-air images data. By the benefit of the color prior and imaging algorithm, the generating model can be with fewer number of parameters and more effective. Meanwhile, a simple yet effective model is proposed to train on the in-air images and corresponding rendered synthetic underwater images for color correction. Different from other image restoration models with multiple substructure or complicated construction, the proposed method has only few convolution layers and combining with a dense-like and a res-like structure. Finally, the enhanced results demonstrate the superiority of the proposed method, which performs favorably against the existing state of the art methods in both effectiveness and model size aspects.
Resource guide for teaching post-quantum cryptography

Public-key cryptography has become a popular way to motivate the teaching of concepts in elementary number theory, abstract algebra, and introduction to proof courses, as well as in cryptography courses. Unfortunately, many experts expect quantum computers to make common forms of public-key cryptography obsolete in the near future. Fortunately, there are several systems being evaluated to replace RSA and the other systems we currently use. While some of the systems are too complicated to be good examples in introductory courses, others are either quite manageable or have simplified versions that are manageable. This article gives a tour of the main types of systems under consideration and the teaching resources available for instructors who want to teach them.
Reward modeling for mitigating toxicity in transformer-based language models

Transformer-based language models can generate fluent text and be efficiently adapted across various natural language generation tasks. However, language models that are pretrained on large unlabeled web text corpora have been shown to suffer from degenerating toxic content and social bias behaviors, consequently hindering their safe deployment. Various detoxification methods have been proposed to mitigate language model toxicity; however, these methods struggle to detoxify language models when conditioned on prompts that contain specific social identities related to gender, race, or religion. In this study, we propose Reinforce-Detoxify, a reinforcement learning-based method for mitigating toxicity in language models. We address the challenge of safety in language models and propose a new reward model that can detect toxic content and mitigate unintended bias towards social identities in toxicity prediction. The experiments demonstrate that the Reinforce-Detoxify method for language model detoxification outperforms existing detoxification approaches in automatic evaluation metrics, indicating that our approach in language model detoxification is less prone to unintended bias toward social identities in generated content.
Boosting Moth-Flame Optimization Algorithm by Arithmetic Optimization Algorithm for Data Clustering

Data clustering (DC) is one of the common data mining problems in which the given objects are grouped into a specific number of clusters. Each cluster contains similar objects, and different clusters contain dissimilar objects. In this research, a novel hybrid clustering method is proposed to solve several common DC problems. The proposed method combined the Moth-Flame Optimization (MFO) algorithm with Arithmetic Optimization Algorithm (AOA), called MFOAOA. The main aim of the proposed MFOAOA is to incorporate the advantages of the exploration search ability of the MFO algorithm and the exploitation search of the AOA. The proposed MFOAOA is tested using several benchmark DC problems and compared with several well-known methods, including AOA, Particle Swarm Optimizer, Gray Wolf Optimizer, Sine Cosine Algorithm, Aquila Optimizer (AO), Whale Optimization Algorithm, and MFO algorithm. The results showed that the proposed MFOAOA obtained better results in all the tested clustering-based approaches.
Reachability Embeddings: Scalable Self-Supervised Representation Learning from Mobility Trajectories for Multimodal Geospatial Computer Vision

Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred to solve a wide variety of downstream supervised tasks. In this work, we propose a self-supervised method for learning representations of geographic locations from unlabeled GPS trajectories to solve downstream geospatial computer vision tasks. Tiles resulting from a raster representation of the earth's surface are modeled as nodes on a graph or pixels of an image. GPS trajectories are modeled as allowed Markovian paths on these nodes. A scalable and distributed algorithm is presented to compute image-like representations, called reachability summaries, of the spatial connectivity patterns between tiles and their neighbors implied by the observed Markovian paths. A convolutional, contractive autoencoder is trained to learn compressed representations, called reachability embeddings, of reachability summaries for every tile. Reachability embeddings serve as task-agnostic, feature representations of geographic locations. Using reachability embeddings as pixel representations for five different downstream geospatial tasks, cast as supervised semantic segmentation problems, we quantitatively demonstrate that reachability embeddings are semantically meaningful representations and result in 4-23% gain in performance, as measured using area under the precision-recall curve (AUPRC) metric, when compared to baseline models that use pixel representations that do not account for the spatial connectivity between tiles. Reachability embeddings transform sequential, spatiotemporal mobility data into semantically meaningful tensor representations that can be combined with other sources of imagery and are designed to facilitate multimodal learning in geospatial computer vision.