Reviewerly/exHarmony huggingface dataset:
Professional Development for ESL Educators During Covid-19 Pandemic Era at Sabah’s Tertiary Institutions, Malaysia

Technology has played a significant role in Malaysian education sector including tertiary institutions during Covid-19 Pandemic.All educators including those who teach English as Second Language (ESL) are able to continue classes during this difficult time through various online platforms such as Google Meet, MS Teams, Zoom and others.This phenomenon undoubtedly demands skills which would enable language educators to effectively incorporate the use of technology and the inculcation of 21 st Century skills in their classrooms.In order to support these educators to embrace these different skills, additional training such as professional development programmes is needed.This literature review will canvass the challenges and opportunities of English Language Teaching in terms of preparation experiences of ESL educators for teaching and learning in 21 st Century as well as the benefits and challenges of conducting professional development programmes at Sabah's tertiary institutions, Malaysia.This literature review is carried out through an analysis of secondary data in the form of related and appropriate previous studies conducted by researchers in the English as Second Language (ESL).The findings found that the ESL educators seem to move towards the 21 st Century, yet additional training through professional development programs are needed to help them embrace the challenges of 21 st Century education at tertiary institutions.This review may help to strengthen ESL educators' teaching skills which indirectly upgrade their quality level while at the same time help them to embrace the 21 st Century during the Pandemic era.
Dataset Geography: Mapping Language Data to Language Users

As language technologies become more ubiquitous, there are increasing efforts towards expanding the language diversity and coverage of natural language processing (NLP) systems.Arguably, the most important factor influencing the quality of modern NLP systems is data availability.In this work, we study the geographical representativeness of NLP datasets, aiming to quantify if and by how much do NLP datasets match the expected needs of the language speakers.In doing so, we use entity recognition and linking systems, presenting an approach for good-enough entity linking without entity recognition first.Last, we explore some geographical and economic factors that may explain the observed dataset distributions. 1 2 Datasets designed to capture dialectal variations, e.g., SD-QA (Faisal et al., 2021), are culturally-aware in terms of annotator selection, but there is no guarantee that their content is also culturally-relevant for the language speakers.3 See §2 of their paper.
Security Analysis of Information Systems Based on Attack Sequences Generation and Testing

The paper considers an issue of analysis of security risks posed by the multistage cyber attacks using known tactics, techniques, and procedures. The technique for cyber attack sequences modeling and testing using source data from the MITRE ATT&CK database and the production model is proposed. Risk assessment is implemented for the generated cyber attack sequences, analysed information system components and an information system as a whole using risks decomposition and Common Vulnerability Scoring System metrics. The developed techniques for cyber attack sequences modeling and risks assessment are implemented within the software tool. The tool also allows recommending possible countermeasures for the successful attack sequences using MITRE ATT& CK data. The experiments for different infrastructures are conducted and application of the developed tool is demonstrated on the case study.
CUET-NLP@DravidianLangTech-ACL2022: Exploiting Textual Features to Classify Sentiment of Multimodal Movie Reviews

With the proliferation of internet usage, a massive growth of consumer-generated content on social media has been witnessed in recent years that provide people's opinions on diverse issues.Through social media, users can convey their emotions and thoughts in distinctive forms such as text, image, audio, video, and emoji, which leads to the advancement of the multimodality of the content users on social networking sites.This paper presents a technique for classifying multimodal sentiment using the text modality into five categories: highly positive, positive, neutral, negative, and highly negative.A shared task was organized to develop models that can identify the sentiments expressed by the videos of movie reviewers in both Malayalam and Tamil languages.This work applied several machine learning (LR, DT, MNB, SVM) and deep learning (BiLSTM, CNN+BiLSTM) techniques to accomplish the task.Results demonstrate that the proposed model with the decision tree (DT) outperformed the other methods and won the competition by acquiring the highest macro f 1 -score of 0.24.
CACLA-Based Local Path Planner for Drones Navigating Unknown Indoor Corridors

This article presents an online local path planning approach for autonomous drone navigating a 2D plane in an unknown, indoor corridor-like environment. The proposed method utilizes a reinforcement learning approach for training a local path planner for navigation in the said environment. With a continuous actor-critic learning automaton (CACLA) applied for continuous action spaces, the proposed algorithm uses a reward structure that formulates a balancing function that gives reward based on balancing the vehicle between artificial potential hills. The drone thereby learns steering control and obstacle avoidance while maintaining a central aligned position with respect to the unknown hallways or corridors. A novel CACLA algorithm and incorporation of a special experience replay memory for the better converging tendency of drone toward the balancing point have been introduced in this article. The proposed reinforcement learning-based online local path planner has been tested on a simulated drone in Gazebo environment.
Hey AI, Can You Solve Complex Tasks by Talking to Agents?

Training giant models from scratch for each complex task is resource-and data-inefficient.To help develop models that can leverage existing systems, we propose a new challenge: Learning to solve complex tasks by communicating with existing agents (or models) in natural language.We design a synthetic benchmark, COMMAQA, with three complex reasoning tasks (explicit, implicit, numeric) designed to be solved by communicating with existing QA agents.For instance, using text and table QA agents to answer questions such as "Who had the longest javelin throw from USA?".We show that black-box models struggle to learn this task from scratch (accuracy under 50%) even with access to each agent's knowledge and gold facts supervision.In contrast, models that learn to communicate with agents outperform black-box models, reaching scores of 100% when given gold decomposition supervision.However, we show that the challenge of learning to solve complex tasks by communicating with existing agents without relying on any auxiliary supervision or data still remains highly elusive.We release COMMAQA, along with a compositional generalization test split, to advance research in this direction. 1
Cross-lingual Inference with A Chinese Entailment Graph

Predicate entailment detection is a crucial task for question-answering from text, where previous work has explored unsupervised learning of entailment graphs from typed open relation triples.In this paper, we present the first pipeline for building Chinese entailment graphs, which involves a novel high-recall open relation extraction (ORE) method and the first Chinese fine-grained entity typing dataset under the FIGER type ontology.Through experiments on the Levy-Holt dataset, we verify the strength of our Chinese entailment graph, and reveal the cross-lingual complementarity: on the parallel Levy-Holt dataset, an ensemble of Chinese and English entailment graphs outperforms both monolingual graphs, and raises unsupervised SOTA by 4.7 AUC points.
Statistical Post-processing Approaches for OCR Texts

Low-quality Optical Character Recognition systems often result in different kinds of errors in OCR-generated texts. Hence, OCR error detection and correction are essential OCR post-processing tasks for improving the OCR text readability and usability. In this paper, we present and discuss the statistical linguistic features obtained from text corpora and OCR text datasets and employed in OCR post-processing approaches. In addition, we show our two statistical language models based on these linguistic features and their OCR error correction performances on two published databases attracting research efforts in text recognition and correction, one database in the ICDAR 2017 OCR post-correction competition and the other database in the Vietnamese online handwriting recognition competition.
BCCT-Based Learning Management in Inclusive Kindergarten (Multi-Site Study in Mawaddah Kindergarten Central Banjarmasin District and Pembina State Kindergarten East Banjarmasin District)

This study aimed to examine BCCT-based learning management in Inclusive Kindergarten on Mawaddah Kindergarten in Central Banjarmasin District and Pembina State Kindergarten in East Banjarmasin District. This was a descriptive qualitative study that used data from the Head of Kindergarten and teachers. The results showed that planning activities involved the annual program preparation, lesson plans, and independent calendar. In contrast, organizing involved the schools' vision, mission, goals, organizational structure, division of teaching tasks and classes, as well as play equipment and learning media inventory. Furthermore, the principal implementation process involved teachers learning various activities, such as opening, closing, and monitoring the learning process. The learning programs were evaluated through student development results, teacher performance, strategies, and follow-up on learning improvement.
A MASTERCLASS IN ADULT EDUCATION: TECHNOLOGY AND SKILLS

The publications of the first quarter of the XXI century talk about the need for creativity, imagination, initiative of an adult. The article examines how masterclasses and festivals of masterclasses are used to develop the relevant qualities. The paper aims to clarify the content of these concepts.&#x0D; The authors used a technological approach to reveal the meaning of the concepts of ‘masterclass’ and ‘a festival of masterclasses.’ The study is based on the provisions of the theory of transformational learning. Master class is understood as a form of organization of adult learning, the essence of which is the learner's skill. According to the analysis result, the festival of masterclasses is characterized as one that relies more on the manufacturability of the learning process.&#x0D; There are two types of master classes: 1) the ones of technological level, which are designed to help in gaining new experience and in the organization of meaningful leisure for adults; 2) the ones of the skill development level, which through the self-development of the learner, invite to the self-development of the learner.&#x0D; Emphasis is placed on the expediency of creating conditions for synergies and self-organization of a team of leading master classes in festivals that understand the educational needs of target groups.&#x0D; It envisages publicity, the presence of an audience that observes the process of interaction between the presenter and the participants of the master class, as well as the festival as a set of masterclasses.&#x0D; It turns out that in this sense, masterclasses and festivals can meet the expectations of adults to learn not only as a form of learning a new experience and the organization of meaningful leisure but as a form of learning for change and transformation, the impetus to development and self-development. In this way, the paradigm of pedagogical creativity is realized.
DEVELOPMENT OF VIRTUAL REALITY TECHNOLOGY: HOME TOUR FOR REAL ESTATE PURCHASE DECISION MAKING

Virtual reality (VR) is a technology; derived with various potential benefits for many aspects of rehabilitation assessment, treatment, and research. The emphasis of VR is computational rather than experiential. To preview Quill Residences houses, a buyer had to go to the showroom that was only located at two places in Kuala Lumpur. The showroom houses often lack depth and a sense of reality and take quite a bit of imagination to realize what the effect will look like in the real environment. There is a less interactive virtual reality walkthrough application from a local company for real estate marketing purposes. Therefore, this research aims to evaluate the effectiveness of interactive virtual reality walkthrough applications in home interior design for the real estate purchase decision. This research focuses on the interior design of Quill Residences and helps the real estate's buyer to identify the home interior design criteria for the purchase decision. The current study has employed the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model representing a home tour immersive framework (HTIF) in creating practical resources to support VR application development. Results have shown that 90% of respondents believe that interactive virtual reality walkthrough application is effective in assisting the real estate's purchase decision. Hence, this study will contribute to the effectiveness of interactive virtual reality walkthrough applications for real estate purchase decisions.
IMPLEMENTASI SUPPORT VECTOR MACHINE DALAM MEMPREDIKSI HARGA RUMAH PADA PERUMAHAN DI KOTA BANDAR LAMPUNG

Machine Learning has been widely used in terms of predictions for analyzing datasets. One method of Machine Learning is Support Vector Machine (SVM). The house has an important role in the survival of human beings. With the times, many developers are competing to build housing. The purpose of this study is to predicted the housing cost using Support Vector Machine. The data in this research used the data of house in Bandar lampung, the price, the location and the building specification. The amount of data used 51 datas and 33 variables with regression and classification, also used 3 kernels and it&amp;#39;s model, 12 times first trial and next 6 experiments done with fitur selection. The trial result was kernel regression polynomial model reached the highest R 2 that was 95,99% linear kernel and gaussian kernel reached R 2 90,99% and 81,43% each. While in accuration classification model trial is obtained in 8 class of gaussian kernel as big as 91,18%, and linear kernel and polynimonal kernel get an accuracy of 90,20% and 89,90%.
Effectiveness of Privacy Techniques in Smart Metering Systems

Smart grid technologies enable timely energy billing for residential homes. The ability to react to energy demands during peak hours allows energy providers to conserve power and operate efficiently. However, these data streams are also susceptible to privacy attacks within the energy company and from outside hackers. We implemented four different privacy models: k-anonymous, l-diversity, t-closeness, and &#x03B5;-differential privacy. We demonstrate the models&#x2019; effectiveness using a real-world dataset composed of 15 different residential households with energy consumption data spanning over a year.
Twitter Sentiment Analysis of Indonesia Internet Service Provider: A Case Study of Indihome and Firstmedia

Every company, especially internet service providers, needs to improve the quality of its products. The initial stage of improving product quality can be done by knowing customer satisfaction as an evaluation material. One way to determine customer satisfaction with a product is by calculating Customer Satisfaction using sentiment analysis on Twitter data, where Twitter is a very popular social media to get customer satisfaction feedback. The sentiment analysis method used is machine learning using a decision tree and random forest. From the two methods, the results show that the random forest method is better than the decision tree method. The oversampling method is also carried out to overcome the imbalanced class data set. The results of the Customer Satisfaction Score obtained show that tweets containing the word "Firstmedia" are better than tweets containing the word "Indihome" with a value of -41.67 for Firstmedia and -59.259 for IndiHome
A User-Centric Multi-Context Hybrid Reasoning Information Retrieval Model

Information Retrieval has been in existence since the 1940s and is impossible to do without. However, current information retrieval systems are known to be ineffective. The reason that seems to encompass this ineffectiveness is design-focused. Designers and developers of Information Retrieval Systems are known to be system focused rather than user-focused. They provide the same information to users even when they are in different contexts and have diverse preferences. Using a restaurant use case, we propose a conceptual user-centric multi-context hybrid reasoning Information Retrieval model to improve the accuracy of retrieved results.
PERSEPSI MAHASISWA KEPERAWATAN TERHADAP PEMBELAJARAN BAHASA INGGRIS DARING PADA MASA PANDEMI COVID-19 DI INSTITUT KESEHATAN DELI HUSADA DELI TUA

The aim of the research is to determine the perception of Nursing students towards online learning in English subject during the Covid-19 Pandemic at Health Institute of Deli Husada Deli Tua Province North Sumatera 2020. The research type is a quantitative descriptive research with the method survey. The data collection technique in this study used the instrument questionnaires and data analysis techniques using descriptive statistics with percentages. The population of the research amounted to 308 students of Nursing at Health Institute of Deli Husada Deli Tua Province North Sumatera using the Proportional technique Random Sampling so that the determination of the number of samples is 15% of the total number of students This number is 47 students at Health Institute of Deli Husada Deli Tua. The validity is 0.585 and the reliability is 0.816, then the instrument in this study was declared reliable. Based on the results of the research as a whole it was concluded Nursing students' perceptions of online learning in English subjects in the Covid-19 Pandemic at Health Institute of Deli Husada Deli Tua in 2020 is in the very category positive percentage 6.38% or 3 students, positive category percentage 21.27% or 10 students, the medium category is the percentage of 42.56% or 20 students, the negative category is the percentage 23.40% or 11 students, and the very negative category the percentage of 6.38% or 3 students. So it can be said that the perception of Nursing students at Health Institute of Deli Husada Deli Tua category is medium.
Application of Transformer in Medical Image Segmentation

Accurate lesion segmentation is of great significance to the diagnosis of patients' illness and the formulation of treatment plan, and the segmentation results can provide doctors with important reference information. However, the focus of medical images is highly similar to the surrounding normal tissues, so the segmentation of medical images is a very challenging task. Transformer is gradually applied to the field of medical image segmentation by researchers with its strong learning ability.
Design and development of drones to autonomously interact with objects in unstructured outdoor scenarios

Aerial drones are systems that have been largely employed in a number of civil applications, from surveillance and environmental monitoring to day-to-day service. In view of this, the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC) held in Abu Dhabi (UAE) was a competition that aimed to push forward the technological limits of the actual aerial devices. We present the design and development of drone prototypes to meet one of the goals of Challenge 1, namely autonomously identifying and attacking randomly-positioned colored targets. This paper reports the main hardware/software design choices to accomplish the highest flexibility and usability of our aerial platforms, namely the strategies to control the overall dynamics and vision for the eventual interaction with targets. We achieved the fourth place among 22 international teams, since we successfully dispatched all the targets, which were dispersed throughout an area of 6000 m2 , in 365 s. Although focused on specific goals, the methodologies developed in view of the competition are suitable for further improvements towards other application fields.
Adversarial Attacks on Vehicle Re-Identification

Integral to intelligent transportation, vehicle re-identification (re-ID) involves a match of the same vehicle across different cameras and therefore has a vital role to play in monitoring and security. Recently, the application of deep learning to computer vision has drawn great attention, inspired by which, deep learning-based vehicle re-ID models have gained popularity and delivered great outcomes. However, it has been proved that a deep neural network could face the problem of adversarial examples, which render a model significantly vulnerable by adding perturbations to an image. Although there are many researches on adversarial attacks on classification tasks, few of them focus on the safety of vehicle re-ID, whose algorithm, as a deep learning algorithm, is most likely to grapple with security flaws. Such attacks pose grave risks as they can barely be detected by humans. A metric attack on the vehicle means modifying the distance between features of attacked images and those of reference images. In this paper, we leveraged the baselineNet, a vehicle re-ID network, to make a comparison among multiple metric learning-based attacks and proposed the Average Furthest-Negative Attack (AFNA) algorithm, which can produce better results at a lower cost of time in re-identifying vehicles. Effects of attacks were evaluated on the VeRi-776 dataset.
Differential Privacy and Bayesian for Context-Aware Recommender Systems

Incorporate contextual information into recommendation systems can obtain better accuracy of recommendation, however, the users’ individual privacy may be disclosed by attackers. In order to resolve this problem, the authors propose a context-aware recommendation system that integrates Differential Privacy and Bayesian Network technologies (DPBCF). Firstly, the paper uses k-means algorithm to cluster items to relieve sparsity of rating matrix. Next, for the sake of protecting users’ privacy, the paper adds Laplace noises to ratings. And then adopts Bayesian Network technology to calculate the probability that users like a type of item with contextual information. At last, the authors illustrate the experimental evaluations to show that the proposed algorithm can provide a stronger privacy protection while improving the accuracy of recommendations.
Indonesian Question Answering System for Factoid Questions using Face Beauty Products Knowledge Graph

Question answering (QA) system is developed to find the right answers from natural language questions. QA systems can be used for building chatbots or even search engines. In this study, we’ve built an Indonesian QA system that uses Anindya Knowledge Graph as its data source. The idea behind this QA system is translating questions into SPARQL queries. The proposed solution consists of four modules, namely question classification, information extraction, token mapping, and query construction. The question classification and the information extraction modules were experimented using SVM, LSTM, and fine-tuning IndoBERT. The text representations were also tested to find the best result among tf-idf, FastText, and IndoBERT. In our experiment, we found that the fine-tuning IndoBERT model had obtained the best performance on both question classification and information extraction modules.