Reviewerly/exHarmony huggingface dataset:
Teaching Effect Evaluation Mechanism of English MOOC Combined with LS-SEM Intelligent System

In order to improve the effect of English teaching, this article analyzes the MOOC teaching system, constructs an evaluation mechanism combined with the LS-SEN intelligent system, and obtains an intelligent English MOOC teaching effect evaluation system. According to the actual needs of English MOOC teaching effect evaluation, this article proposes an integrated method LS-Ensem algorithm for continuous modeling from the perspective of gradient descent. At the same time, this article uses the commonly used variance function as the loss function, combined with repeated iterations to improve the running accuracy of the algorithm. In addition, this article constructs an intelligent system structure based on the actual needs of English MOOC teaching. According to the experimental research, the teaching effect evaluation mechanism of English MOOC combined with the LS-SEM intelligent system can play an important role in English MOOC teaching.
Two-phase Multi-document Event Summarization on Core Event Graphs

Succinct event description based on multiple documents is critical to news systems as well as search engines. Different from existing summarization or event tasks, Multi-document Event Summarization (MES) aims at the query-level event sequence generation, which has extra constraints on event expression and conciseness. Identifying and summarizing the key event from a set of related articles is a challenging task that has not been sufficiently studied, mainly because online articles exhibit characteristics of redundancy and sparsity, and a perfect event summarization needs high level information fusion among diverse sentences and articles. To address these challenges, we propose a two-phase framework for the MES task, that first performs event semantic graph construction and dominant event detection via graph-sequence matching, then summarizes the extracted key event by an event-aware pointer generator. For experiments in the new task, we construct two large-scale real-world datasets for training and assessment. Extensive evaluations show that the proposed framework significantly outperforms the related baseline methods, with the most dominant event of the articles effectively identified and correctly summarized.
Non-metric correction method for complete lens distortion using dual-characteristics joint measurement

In practical vision systems, due to the limitation of manufacturing technology, charge-coupled device pixel cells are difficult to satisfy the standard square, and the lens inevitably has imaging nonlinearity, which leads to geometric distortion of the image. To ensure the accuracy of subsequent visual processing, it is necessary to correct the image distortion. A non-metric correction method for complete lens distortion using dual-characteristics joint measurement is proposed. We not only considered the influence of the distortion center in the correction process but also constructed a mapping matrix to standardize the pixel cells, thus establishing a complete lens distortion model. Simultaneously, the linear residual function of the dual-characteristics joint measurement was defined according to the geometric constraint between the vanishing point and the straightness. In addition, the distortion model coefficients were determined by nonlinear iterative optimization. Extensive experiment results demonstrated that the proposed method enables effective and accurate lens distortion correction, which is significantly superior to the existing single characteristic measurement distortion correction approach.
AN OVERVIEW OF REMOTE TEACHING: EMPLOYING ACTIVE LEARNING TO ENGAGE STUDENTS DURING COVID-19 PANDEMIC IN HIGHER EDUCATION

Remote teaching is the “new normal” especially in higher educational institutions as the world grapples with the raging COVID-19 pandemic and innumerable changes caused by disruptive technologies. Higher educational institutions have resorted to remote teaching as a means of ensuring student safety and an effective learning approach. For this approach to succeed, didactic methods must be carefully chosen to ensure student active engagement during learning processes. Specifically, cognitive engagement has been often cited as a critical component of students’ educational experience which includes active, constructive, interactive, and passive cognitive modes of engagement. These modes allow students to demonstrate knowledge as node-link structures through different overt behavioural knowledge-change processes that require individuals to store, activate, link, and infer varying stages of learning also known as Active Learning (AL). AL enables students’ engagement activities to construct knowledge, improve subject content retention and better their performance in achieving learning outcomes. AL can leverage both synchronous and asynchronous teaching pedagogical methods. The primary goal of this review was to examine AL literature during COVID-19 pandemic in order to propose solutions for improving student cognitive engagement in higher educational institutions.
THE APPLICATION OF MNEMONIC STRATEGIES TO IMPROVE EFL STUDENTS’ APTITUDE FOR VOCABULARY

Background and Purpose: Using mnemonic strategies is one of the useful ways to help students memorize and retrieve vocabulary effectively in learning English. This paper mainly focuses on promoting mnemonic strategies to EFL students’ vocabulary learning. Though some research in this field has been conducted, there have been hardly any specific studies on using mnemonic strategies to improve vocabulary learning ability for English major pre-intermediate level students at a university of foreign languages in Vietnam. The study aims to investigate the benefit of mnemonic strategies in vocabulary learning for EFL students.&#x0D; &#x0D; Methodology: This study was designed as quasi-experimental research, employing a mixed-method approach to gather data. 90 respondents from a university in the centre of Vietnam were recruited in the study using a simple random sampling method. A 5-point Likert scale questionnaire of 20 items, a vocabulary test and semi-structured interview questions were the research instruments used in the study. The Statistical Package for Social Sciences (SPSS) version 25.0 was used for analyzing the quantitative data. The responses from semi-structured interviews were analysed in theme.&#x0D; &#x0D; Findings: The results of the study showed that both teacher-provided mnemonic strategies and student-generated strategies could help students memorize vocabulary effectively. However, students who were creative and methodical in applying their own mnemonic strategies performed better at memorizing vocabulary. Also, those students who applied mnemonic strategies had better results in the vocabulary tests than the others.&#x0D; &#x0D; Contributions: The study concludes that mnemonic strategies including spatial grouping, keyword method, the use of rhyme, story method, acronym, etc. to memorize and retrieve vocabulary has potential to be applied with EFL adult students as a new vocabulary teaching method. The research is expected to contribute to university administrators, EFL instructors, as well as university students to encourage and apply mnemonic strategies for improving EFL students’ vocabulary learning.&#x0D; &#x0D; Keywords: Mnemonic strategies, vocabulary learning, pre-intermediate level, student, English.&#x0D; &#x0D; Cite as: Thảo, T. M. (2022). The application of mnemonic strategies to improve EFL students’ aptitude for vocabulary. Journal of Nusantara Studies, 7(2), 166-183. http://dx.doi.org/10.24200/jonus.vol7iss2pp166-183
Passages

Ross Anderson's Security Engineering (now in its third edition, but the second edition is reviewed here; I have only glanced at the third edition, but am certain it is also excellent, and more up-to-date) is something this column seldom considers: a fairly exhaustive, very long, and quite technical book on an actual computer science topic. It is also a book any serious "lay reader" can enjoy, unlike some of the other more technical software engineering classics we've looked at previously.
Impact of Online Education and Sentiment Analysis from Twitter Data using Topic Modeling Algorithms

During a pandemic, all industries suffer greatly, and every sector of the world suffers in some way, including the education sector. Internet expressions reflect users' feelings about a product or service. The polarity of information in source data toward a subject under investigation is determined by sentiment analysis processes. The goal of this study is to examine social media expressions about online teaching and learning, as online education will become a part of everyday life in the future. We collected data from Twitter using keywords related to online education and Google form from engineering undergraduate students for prototype implementation. This analysis will assist teachers, parents, and the student community in understanding the benefits and drawbacks of the education industry, allowing for further improvement in educational outcomes. We used aspect-based sentiment analysis and topic modeling to determine sentiment polarity and important topics for education sector stakeholders. To begin, we used TextBlob Python package to determine sentiment polarity, and Bag of Words, LDA and LSA model for discovering topics. After modeling topics from the collected data, topic Coherence is used to assess the degree of semantic similarity between high scoring words in the topic. The word cloud and LDAvis are used to visualize data. The experimental results are promising and it will assist education stakeholders in addressing the concerns that have been identified as social media expressions to work on. Since the boom in science and technology, humans have been trying to invent machines that could reduce their efforts in day to day activities. In this paper, we develop a personal assistant robot that could pick up objects and return it to the user. The robot is controlled using an android application in mobile phones. The robot can listen to user’s command and then respond in the best way possible. The user can command the robot to move to given location, capture images and pick objects. The robot is equipped with ultrasonic sensor and web camera that helps it to move to different location effectively. It is also equipped with sleds that play important role in object picking process. The robot uses a tiny YOLOv3 model which is rigorously trained on several images of the object. There are some possible improvements that can be achieved which could help this robot to be used in several other fields as well.
LCMQSINABM: Design of a Low-power hybrid Consensus Method for QoS-aware Sidechain-based IoT Networks via Augmented Bioinspired computing Models

Security is one of the primary issues in any wireless network deployment, because, wireless nodes are susceptible to a wide-variety of internal &amp; external attacks. These include improper authentication &amp; access control, distributed denial of service (DDoS), sybil, spoofing, spying, masquerading, etc. Securing networks against these attacks requires an immutable, transparent, traceable, and distributed computing security model, that has minimum overheads. Most of these characteristics are fulfilled via use of blockchains, which assist in low-complexity deployment of security &amp; hashing models. But the delay needed to scale these blockchains increases exponentially w.r.t. chain length, due to which researchers split the main blockchain into multiple sidechains. A wide variety of models are proposed by researchers for sidechain formation, and most of them are consensus dependent, which limits the scalability. Moreover, energy needed to mine blocks for these sidechains depends directly on the consensus model, encryption model, hash rules, and length of the blockchain. Thus, models proposed for sidechain formation are context-specific, and have limited scalability performance when used for multiple blockchain types. To overcome these limitations, and maintain high security performance, this text proposes design of a Low-power hybrid Consensus Method for QoS-aware Sidechain-based IoT Networks via Augmented Bioinspired computing Models. This method uses a combination of Proof-of-Work (PoW), Proof-of-Stake (PoS) &amp; Proof-of-Authority (PoA) based consensus models, which assists in reducing its mining delay. The PoW model allows selection of nodes with higher performance, PoS allows selection of nodes with higher stake, and PoA ensures better control of IoT devices. Selection of these consensus models is done via use of an Improved Genetic Algorithm (IGA) model, that evaluates the power needed for mining, and minimizes it using a rule-based method. This is combined with a sidechain creation model, that assists in improving QoS performance via dynamically splitting the main blockchain into performance-specific sidechains. These sidechains are categorized into low-power, low-delay, and high-throughput sidechains, which are formed via Elephant Herding Optimization (EHO) model. Due to combination of IGA for consensus selection, and EHO for sidechain creation, the proposed model is able to reduce energy needed for mining by 15.4% when compared with various state-of-the-art models. It is also able to improve mining speed by 5.3%, while maintaining high security performance under different IoT Network attacks. Due to this performance enhancement, the proposed model is capable of being deployed for a wide variety of low-delay healthcare IoT, high-throughput industrial IoT, and low energy home IoT application deployments.
THROUGH THE LENS OF YOUNG SARAWAKIAN ENGLISH AS SECOND LANGUAGE LEARNERS: IDENTIFYING THEIR LANGUAGE LEARNING STRATEGIES

Background and Purpose: Different language learners have varying capacities to acquire L2, where some learners develop their learning strategies faster than others. Hence, it is critical for instructors to examine the degree of understanding and ability obtained by the learners through the investigation of their language learning strategies particularly on the type of schools the learners attend. This study, therefore, reveals frequently employed learning strategies in ESL classrooms based on the type of school the learners attend, notably the distinction between Cluster School of Excellence (CSE) and non-cluster primary school in Malaysia.&#x0D; &#x0D; Methodology: This quantitative research gathered information through the administration of a survey questionnaire. In total, 112 young ESL Malaysian learners (CSE:56; Non-CSE:56) aged 11 were chosen through stratified random sampling in Samarahan, Sarawak. The close-ended Strategy Inventory for Language Learning (SILL) that consist of 50 questions was used to collect information and SSPS Version 25 was used to interpret an independent t-test to determine the significant in strategies employed by the respondents.&#x0D; &#x0D; Findings: The data revealed that the most prevalent Language Learning Strategy (LLS) used by young Malaysian ESL learners are metacognitive strategies that allow learners to manage their learning. Learners in non-CSE school adopt learning strategies more than learners in CSE school and there is no significant difference in the type of learning strategies employed by both learners.&#x0D; &#x0D; Contributions: School’s environment reflects a significant impact on the academic success of students and the result of the investigation helped throughout the educational process to tailor teaching strategies to help learners achieve common educational goals. Teachers from both schools should use this chance to conceive, construct, or assign tasks that will push students to reach their full potential in language learning.&#x0D; &#x0D; Keywords: Language learning, language learning strategies, type of school, Malaysian young learner.&#x0D; &#x0D; Cite as: Sani, S., &amp; Ismail, H. (2022). Through the lens of young Sarawakian English as second language learners: Identifying their language learning strategies. Journal of Nusantara Studies, 7(2), 399-423. http://dx.doi.org/10.24200/jonus.vol7iss2pp399-423
Text Extraction from Image

Text extraction is one of the key tasks in document image analysis. Automatic text extraction without characters recognition capabilities is to extract regions just contains text. The text extraction process includes detection, localization, segmentation and enhancement of the text from the given input image. In this paper we present a comparative study and performance evaluation of various text extraction techniques.
PROBABILISTIC ENTAILMENT ON FIRST ORDER LANGUAGES AND REASONING WITH INCONSISTENCIES

Abstract We investigate an approach for drawing logical inference from inconsistent premisses. The main idea in this approach is that the inconsistencies in the premisses should be interpreted as uncertainty of the information. We propose a mechanism, based on Kinght’s [14] study of inconsistency, for revising an inconsistent set of premisses to a minimally uncertain, probabilistically consistent one. We will then generalise the probabilistic entailment relation introduced in [15] for propositional languages to the first order case to draw logical inference from a probabilistic set of premisses. We will show how this combination can allow us to limit the effect of uncertainty introduced by inconsistent premisses to only the reasoning on the part of the premise set that is relevant to the inconsistency.
EVOLUTION EXISTS, AT LEAST IN MUSICAL INSTRUMENTS

Charles Darwin could have more successfully applied his theory of evolution to the technological development evident in Musical Instru-ments. Even the modern violin, so apparently similar to that of the 1600s, has actually been largely modified and for reasons similar to those that Darwin wanted to attribute to the origin of the species. Following his logic, what will the Violin of the future be? The new ergonomic violin model developed by the Santa Cecilia Conservatory in Rome may be the answer. Let us consider the concerto for violin and orchestra. From the Baroque era to Mozart, then from Beethoven to Tchaikovsky up to Sibelius and Khachaturian, the solo violinist had to deal with ever increasing acoustic needs that over time have been satisfied by continuous changes and updates in the construction of violins. Even the greatness of the theaters and the way in which the music was written progressively required more and more power and higher quality of sound: will classical lutherie be able to answer? The laboratory of the Lutherie Course of the Santa Cecilia Conservatory in Rome has created a new ergonomic model of violin that was conceived with the express purpose of producing more sound with a richer timbre. We are pleased to present it in this article. KEYWORDS. Evolution, Lutherie, Violin, Sound, Violin, Music
Development of Online Service Booking Mobile Application Using Android Studio

This research is all about an application developed in order to make household services more manageable and easy like never before. We have developed an application for Online Service Booking native android application using android studio new version. This paper will also describe the working principle of this app. Admin panel of the application has been developed in PHP language with the help of Code Igniter framework a perfect tool to develop this kind of application. PHPMYADMIN has been used for the development of the database. Also we have used the hosting facility provided by 000webhost to deploy our code onto the server. Google firebase has been used for connecting our both the application as well as the admin panel all together. This application has many type of services. User can choose services according to their need and convenience. They can also select the date and time and the payment modes as per their preference.
THE EFFECT OF EXPLICIT FORM-FOCUSED INSTRUCTION ON THE IMPLICIT KNOWLEDGE OF MALAYSIAN ENGLISH AS SECOND LANGUAGE LEARNERS

Background and Purpose: Explicit form-focused instruction has significantly contributed to second language learners’ explicit knowledge. However, the effect of explicit form-focus instruction (FFI) on second language learners’ implicit knowledge of grammatical forms has been argued by scholars. This study examined the effectiveness of explicit FFI on implicit knowledge of ESL learners.&#x0D; &#x0D; Methodology: This quasi-experimental research was conducted at a selected international school in the Klang Valley, Malaysia. The collected data were analysed through the application of ANCOVA and Scheffe’s test.&#x0D; &#x0D; Findings: The findings showed that when explicit FFI was implemented in the form of metalinguistic information and metalinguistic explanation of the target form, the implicit knowledge of students would not increase. This suggested that the role of explicit FFI as the most effective and reliable tool to enhance implicit knowledge of learners should be doubted.&#x0D; &#x0D; Contributions: The theoretical implication of this study indicates that explicit FFI does not contribute to development of the second language implicit knowledge. Pedagogically, this study’s findings could be beneficial either for teachers looking for an effective method of teaching or for researchers to utilise it in their studies.&#x0D; Keywords: Form-focused instruction (FFI), explicit FFI, implicit knowledge, explicit knowledge&#x0D; &#x0D; Cite as: Hojjat, M., &amp; Hasim, Z. (2022). The effect of explicit form-focused instruction on the implicit knowledge of Malaysian English as second language learners. Journal of Nusantara Studies, 7(2), 61-80. http://dx.doi.org/10.24200/jonus.vol7iss2pp61-80
Highly distributed and privacy-preserving queries on personal data management systems

Personal data management system (PDMS) solutions are flourishing, boosted by smart disclosure initiatives and new regulations. PDMSs allow users to easily store and manage data directly generated by their devices or resulting from their (digital) interactions. Users can then leverage the power of their PDMS to benefit from their personal data, for their own good and in the interest of the community. The PDMS paradigm thus brings exciting perspectives by unlocking novel usages, but also raises security issues. An effective approach, considered in several recent works, is to let the user data distributed on personal platforms, secured locally using hardware and/or software security mechanisms. This paper goes beyond the local security issues and addresses the important question of securely querying this massively distributed personal data. To this end, we propose DISPERS, a fully distributed PDMS peer-to-peer architecture. DISPERS allows users to securely and efficiently share and query their personal data, even in the presence of malicious nodes. We consider three increasingly powerful threat models and derive, for each, a security requirement that must be fulfilled to reach a lower-bound in terms of sensitive data leakage: (1) hidden communications, (2) random dispersion of data and (3) collaborative proofs. These requirements are incremental and, respectively, resist spied, leaking or corrupted nodes. We show that the expected security level can be guaranteed with near certainty and validate experimentally the efficiency of the proposed protocols, allowing for adjustable trade-off between the security level and its cost.