Reviewerly/exHarmony huggingface dataset:
Relationship between Self-Efficacy, Emotional Intelligence, and Parental Social Support with Learning Motivation of University Students in Distance Learning during the Covid-19 Pandemic

Distance learning has an impact on decreasing university students’ motivation to learn. Meanwhile, learning motivation is fundamental in the distance learning process. Learning motivation correlates with internal factors such as self-efficacy and emotional intelligence and external factors such as parental social support. This study aimed to find alternative factors that support learning motivation and examine the relationship between self-efficacy, emotional intelligence, and parental social support with learning motivation in university students carrying out distance learning during the COVID-9 pandemic. The study used quantitative research with the correlational research type involving undergraduate students at the Muhammadiyah University of Surakarta with a total of 408 students. The Academic Motivation Scale-Short version (AMS) was used to measure learning motivation, the General Self-Efficacy Scale (GSES) was used to measure self-efficacy, the Trait Emotional Intelligence Questionnaire-Short Form (TEIQUE-SF) was used to measure emotional intelligence, and the parental social support scale compiled by Fitrotin was used to measure parental social support. Data were analyzed using multiple linear regression tests. The results of the study found that self-efficacy, emotional intelligence, and parental social support had a relationship with learning motivation. This was indicated by the positive value of Beta=27.284, p-value of 0.000 (p &lt;0.005), the regression (F value) of 36.569, the correlation (R)= 0.467, and the effective contribution to learning motivation of 21.8%. Programs to increase self-efficacy, emotional intelligence, and parental social support are important to increase motivation.
Pre-service Training for Students’ Coping Capacity in the Workplace through Research and Development of an E-store Game-based Education System

The pre-service education systems and research papers about level games, which give automatic answers and instant evaluation, are scarce. The system developed in this study could timely judge whether the answers chosen by students are right or wrong when they try to pass game levels and automatically provide the correct answers. In this study, learning units, such as store management practice, circulation management, and mathematics, were comprehensively designed into an animated game for students to play as new employees before beginning employment. Furthermore, the students were allowed to experience the problems and situations that shop service staff may encounter in the workplace. In this way, the students could increase their knowledge and experience in-store operation and customer relationship management in the workplace in advance. Thereby, the students could give better spontaneous responses after entering the workplace in the future. By using the level game designed by this study, students could also improve their service efficiency and professionalism, including store management and operation, as well as the intuition and insight of estimation and number sense ability. This level game contributes to pre-service education.
MAKSIM KUANTITAS DAN MAKSIM KUALITAS SERTA IMPLIKATURNYA PADA TAYANGAN MATA NAJWA EPISODE “IRONI HARI-HARI INI”

This study aims to describe the observance of maxims of quantity and maxims of quality, describe violations of maxims of quantity and maxims of quality, and describe the implicatures found in Mata Najwa's episode of "Ironi Hari-Hari Ini". The research data is in the form of utterances spoken by the resource person and also the presenter, namely, Najwa Shihab. Sources of data obtained by watching the show and making a transcript of the data. The data collection technique used is listening, free involvement, conversation and notes. Analysis of research data was carried out with interactive analysis techniques. While the validity of the data is done by data triangulation. The results showed that the process of compliance and violation of the maxim of quantity and maxim of quality as well as their implicatures in the Mata Najwa episode of "Ironi Hari-Hari Ini" included: (1) obedience to the maxim of quantity was 28.1%; (2) compliance with maxim of quality as much as 7.8%; (3) the violation of the maxim of quantity is 25%; (4) violation of maxim of quality as much as 10.9%; (5) conventional implicature as much as 17.1%; (6) non-conventional implicatures as much as 10.9%. With the background of the different sources, this is one of the reasons for the many compliances and violations of maxims that were found.Keywords: Implicature; Maxim of Quality; Maxim of Quantity; Najwa Eyes.
Evaluation of Digital Literacy to Educating Post-Millennials

This paper is meant to evaluate the usefulness of digital literacy to learners who are classified as post-millennials. It strived to explore a possible retooling of education through innovative strategies to be able to increase value in classroom learning. Utilizing information and technology in communication to search, create, analyze and relay messages which requires cognitive and technical skills, is known as digital literacy. Post-millennials are those born from 1997 onwards. Since their generations are into the maximum use of the internet and technology in communication, the author deemed it necessary to evaluate the digital literacy of the post-millennials in a private school in one of the provinces in the Philippines. The respondents are the administrators, non-teaching staff, teaching staff, and students of this respondent school. The author utilized survey questionnaires to collect data from the participants. Results showed that less than half of the educators who participated favored digital teaching to students. However, this is not the case for learners because they all voted for digital learning. They mentioned that using digital tools is flexible, and they were more favorable in its integration into their classroom learning. The author, based on the findings, concluded that this present generation is more inclined to digital learning and teaching approaches. The author recommends that further and additional research is needed to determine more productive strategies for the provision of internet connection in schools and physical classrooms of the country.
A Novel Self-Adaptive Cooperative Coevolution Algorithm for Solving Continuous Large-Scale Global Optimization Problems

Unconstrained continuous large-scale global optimization (LSGO) is still a challenging task for a wide range of modern metaheuristic approaches. A cooperative coevolution approach is a good tool for increasing the performance of an evolutionary algorithm in solving high-dimensional optimization problems. However, the performance of cooperative coevolution approaches for LSGO depends significantly on the problem decomposition, namely, on the number of subcomponents and on how variables are grouped in these subcomponents. Also, the choice of the population size is still an open question for population-based algorithms. This paper discusses a method for selecting the number of subcomponents and the population size during the optimization process (“on fly”) from a predefined pool of parameters. The selection of the parameters is based on their performance in the previous optimization steps. The main goal of the study is the improvement of coevolutionary decomposition-based algorithms for solving LSGO problems. In this paper, we propose a novel self-adapt evolutionary algorithm for solving continuous LSGO problems. We have tested this algorithm on 15 optimization problems from the IEEE LSGO CEC’2013 benchmark suite. The proposed approach, on average, outperforms cooperative coevolution algorithms with a static number of subcomponents and a static number of individuals.
Efficient Sequential and Parallel Prime Sieve Algorithms

Generating prime numbers less than or equal to an integer number m plays an important role in many asymmetric key cryptosystems. Recently, a new sequential prime sieve algorithm was proposed based on set theory. The main drawback of this algorithm is that the running time and storage are high when the size of m is large. This paper introduces three new algorithms for a prime sieve based on two approaches. The first approach develops a fast sequential prime sieve algorithm based on set theory and some structural improvements to the recent prime sieve algorithm. The second approach introduces two new parallel algorithms in the shared memory parallel model based on static and dynamic strategies. The analysis of the experimental studies shows the following results. (1) The proposed sequential algorithm outperforms the recent prime sieve algorithm in terms of running time by 98% and memory consumption by 80%, on average. (2) The two proposed parallel algorithms outperform the proposed sequential algorithm by 72% and 67%, respectively, on average. (3) The maximum speedups achieved by the dynamic and static parallel algorithms using 16 threads are 7 and 4.5, respectively. As a result, the proposed algorithms are more effective than the recent algorithm in terms of running time, storage and scalability in generating primes.
Scene text recognition via context modeling for low-quality image in logistics industry

Abstract Text recognition has been applied in many fields recently, such as robot vision, video retrieval, and scene understanding. However, minimal research has been conducted in the field of logistics wherein images of express sheets captured by cameras are mostly curved, distorted, and have low resolution. In this study, a new method is proposed to address the aforementioned research gap while simultaneously considering irregular and low-resolution English letters. The entire approach comprises a rectification module, a convolutional neural network (CNN) extractor, a semantic context module (SCM), a global context module (GCM), and a lightweight transformer decoder that can exhibit improved training speed. In particular, we propose the idea of context modeling in our proposed method. (1) The proposed SCM is introduced to capture full-image dependencies and generates rich semantic context information. (2) We propose the GCM, which not only enhances long-range dependencies from the output of SCM but also outputs abundant pixel information to the self-attention decoder. (3) To solve the low-resolution text recognition problem in a large number of express sheet scenes, we propose Chinese datasets for improving intelligent logistics. Experiments conducted on six public benchmarks demonstrate that the developed method achieves better robustness to low-resolution and irregular text images.
Harnessing the digital space

Harnessing the digital space - IJLSIT- Print ISSN No: - 2582-1555 Online ISSN No:- 2456-9623 Article DOI No:- 10.18231/j.ijlsit.2022.012, IP Indian Journal of Library Science and Information Technology-IP Indian J Libr Sci Inf Technol
Language Learning through Digital Media: Investigating the Strategies among Selected International Students in the Philippines

This study attempts to investigate the language learning strategies used by international students in Manila, Philippines, in five areas: planning and organizing, remembering, thinking and understanding, compensating, and interacting. Using the snowball sampling technique, the researcher identified and interviewed ten (10) international students from different colleges and universities for this descriptive research. The findings revealed that the use of digital media has become common for participants in various areas of language learning strategies. For instance, the usefulness of Google Translate, Dictionary+, YouTube, Netflix, mobile language translators, and other language learning applications such as Mango, Drops, Rosetta Stone, Babbel and Mondly were recognized, alongside writing new words, interacting with people, and conversing using a second language (L2). Given the significance of digital media in L2 learning, the researcher developed the digital media-assisted language learning (DMALL) paradigm to contribute to language learning research. This study will cater to a specific context to address the needs of international students in the Philippines. Hence, determining the difficulties and the strategies will form an integral part of the academic curriculum plan or design that will substantially assist the L2 needs of the growing number of international students in the country.
ROLE OF KNOWLEDGE ECONOMY IN ECONOMIC GROWTH: “AN EMPIRICAL STUDY” ON SELECTED ARAB COUNTRIES

Despite the mounting importance of the knowledge economy in diversifying Arab economies, the knowledge transition in these countries is still in its early stages, with a long road ahead. The study’s main aim is to examine the role of the knowledge economy in economic growth and to recognize the most influential knowledge pillar for a sample of 11 Arab countries during the period 2000–2020, namely: Algeria, Bahrain, Egypt, Jordan, Kuwait, Lebanon, Morocco, Oman, Qatar, Saudi Arabia, and Tunisia. The panel generalized least squares model, with its two methods (fixed-effects model and random-effects model), was performed using an extended version of Chen and Dehlman’s (2004) empirical framework. The fixed regression findings confirmed the positive and significant role of knowledge in stimulating economic growth. The estimated results of the random regression revealed that knowledge pillars like innovation, information and communication technology, economic and institutional regimes, and the socio-economic sustainability index have a positive and significant impact on Arab economic growth, whereas education has a significant negative relationship with GDP. This can be attributed to poor quality, unequal access, and infrastructure, as well as the COVID-19 pandemic, which has had a negative impact on education. The results also showed that, in comparison to the other four pillars, the socio-economic sustainability index, which was added to the benchmark Knowledge Economy Index, was the most notable pillar that participated in the examined relationship. As a result of these findings, this study contributes to broadening the scope of Arab countries through investing in key knowledge pillars that promote economic growth and where policies should place a greater emphasis on developing education. Furthermore, because of their critical and significant role in exploring the knowledge-growth relationship, sustainability dimensions must be explicitly included in the knowledge framework.
Twitter interaction between audiences and influencers. Sentiment, polarity, and communicative behaviour analysis methodology

Twitter is one of several social networks with the highest numbers of users in Spain. In spite of this, how are communicative relationships developed in the digital environment among influencers who have emerged on the Internet? These personalities have a stronger influence on children and young people than traditional celebrities. The aim of this work is to study the communicative interaction generated on the profiles of Spanish influencers with the most followers on Twitter, based on the number of content items generated and the responses they receive from users. The polarity and sentiment conveyed by these communications have also been analysed. By processing publications in real time using machine learning based on sentiment analysis, 48,878 tweets and retweets from five influencers were studied over a period of 40 days. The results show that the publications reached nearly 200 million followers, and despite being fourth in terms of the number of followers, @IbaiLlanos is the influencer who leads the conversations on Twitter with the highest number of tweets, retweets, and audience share. Among the most popular topics, sporting events stand out. This study has also confirmed that the most frequently stated emotion is surprise, and that positive messages prevail over those that are negative and neutral with regard to polarity. Nevertheless, the linear regression data has verified that the main trend is toward publishing negative messages, with a lower statistical correlation, which is a behaviour that might possibly be duplicated on other social networks.
Considerations for meaningful sign language machine translation based on glosses

Automatic sign language processing is gaining popularity in Natural Language Processing (NLP) research (Yin et al., 2021). In machine translation (MT) in particular, sign language translation based on glosses is a prominent approach. In this paper, we review recent works on neural gloss translation. We find that limitations of glosses in general and limitations of specific datasets are not discussed in a transparent manner and that there is no common standard for evaluation. To address these issues, we put forward concrete recommendations for future research on gloss translation. Our suggestions advocate awareness of the inherent limitations of gloss-based approaches, realistic datasets, stronger baselines and convincing evaluation.
Overcoming fundamental bounds on quantum conference key agreement

Twin-Field Quantum Key Distribution (TF-QKD) enables two distant parties to establish a shared secret key, by interfering weak coherent pulses (WCPs) in an intermediate measuring station. This allows TF-QKD to reach greater distances than traditional QKD schemes and makes it the only scheme capable of beating the repeaterless bound on the bipartite private capacity. Here, we generalize TF-QKD to the multipartite scenario. Specifically, we propose a practical conference key agreement (CKA) protocol that only uses WCPs and linear optics and prove its security with a multiparty decoy-state method. Our protocol allows an arbitrary number of parties to establish a secret conference key by single-photon interference, enabling it to overcome recent bounds on the rate at which conference keys can be established in quantum networks without a repeater.
Frustratingly Easy Label Projection for Cross-lingual Transfer

Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer. For tasks that involve span-level annotations, such as information extraction or question answering, an additional label projection step is required to map annotated spans onto the translated texts. Recently, a few efforts have utilized a simple mark-then-translate method to jointly perform translation and projection by inserting special markers around the labeled spans in the original sentence. However, as far as we are aware, no empirical analysis has been conducted on how this approach compares to traditional annotation projection based on word alignment. In this paper, we present an extensive empirical study across 57 languages and three tasks (QA, NER, and Event Extraction) to evaluate the effectiveness and limitations of both methods, filling an important gap in the literature. Experimental results show that our optimized version of mark-then-translate, which we call EasyProject, is easily applied to many languages and works surprisingly well, outperforming the more complex word alignment-based methods. We analyze several key factors that affect the end-task performance, and show EasyProject works well because it can accurately preserve label span boundaries after translation. We will publicly release all our code and data.
Amplitude Amplification for Optimization via Subdivided Phase Oracle

We propose an algorithm using a modified variant of amplitude amplification to solve combinatorial optimization problems via the use of a subdivided phase oracle. Instead of dividing input states into two groups and shifting the phase equally for all states within the same group, the subdivided phase oracle changes the phase of each input state uniquely in proportion to their objective value. We provide visualization of how amplitudes change after each iteration of applying the subdivided phase oracle followed by conventional Grover diffusion in the complex plane. We then show via numerical simulation that for normal, skew normal, and exponential distribution of objective values, the algorithm can be used to amplify the probability of measuring the optimal solution to a significant degree independent of the search space size. In the case of skew normal and exponential distributions, this probability can be amplified to be close to unity, making our algorithm near deterministic. We then modify our algorithm in order to demonstrate how it can be extended to a broader set of objective value distributions. Finally, we discuss the speedup compared to classical schemes using the query complexity model, and show that our algorithm offers a significant advantage over these classical approaches.
An adaptive algorithm based on principal component analysis-deep learning for anomalous events detection

One of the most often used applications of human activity detection is anomaly detection, which is covered in this paper. Providing security for a person is a key issue in every community nowadays because of the constantly expanding activities that pose danger, from planned violence to harm caused by an accident. Existing classical closed-circuit television considered is insufficient since it needs a person to stay awake and constantly monitor the cameras, which is expensive. In addition, a person's attention decreases after a certain time. For these reasons, the development of an automated security system that can identify suspicious activities in real-time and quickly aid victims is required. Because identifying activity must be with high accuracy, and in the shortest possible time. We adopt an adaptive algorithm based on the combination of machine learning (ML), principal component analysis (PCA) and deep learning (DL). The UCF-crime dataset was used for the experimentation in this work. Where the area under the curve (AUC) with the proposed approach was equal to 94.21% while the detection accuracy was equal to 88.46% on the test set database. The suggested system has demonstrated its robustness and accomplishment of the best accuracy when compared with earlier designed systems.
Digital forensics on Tencent QQ-instant messaging service in China

Data packet analysis targeting instant messaging (IM) applications has become one of the most mentioned case examples in the digital forensic industry, considering that the forensic engineers can extract valuable information by analysing the data packets used by the IM software. The crucial part of this process is to accomplish a series of research and investigation, in addition to correctly implement the related forensics tools. This paper is intended to use QQ, a popular IM software in China, as an experiment example, in cooperation with various tools from Kali Linux, a digital forensics-oriented Linux distribution, to present the complete process of the data packet analysis operation. The result concludes from the experiment may be able to provide constructive suggestions to other related digital forensics cases.
Immersive Analytics Spaces and Surfaces

Immersive Analytics has now fully emerged as a research topic in the Visualisation and Human-Computer Interaction research communities. While evidence of its benefits has been accumulating, we have still attained only a basic understanding of the extent to which they can support human sensemaking, This workshop aims to define a roadmap for new directions in leveraging the benefits of spatial interaction to support sensemaking. In particular the main goal of this workshop will be to focus on understanding the benefits and applications of immersive interactive spaces and surfaces (e.g. body, walls, smartphones or other interactive surfaces such as tabletops) for enhancing human sensemaking.