Reviewerly/exHarmony huggingface dataset:
Deep neural network supervision and data flow testing

In this issue as a special issue of ICST 2021, we are pleased to present two papers, one for supervision of deep neural network and the other for data flow testing. The first paper, “Uncertainty quantification for deep neural networks: An empirical comparison and usage guidelines” by Michael Weiss and Paolo Tonella, presents an overview of main approaches to uncertainty estimation for supervision of deep neural networks (DNN) along with their pros and cons, a large empirical study for comparing the alternative approaches to uncertainty estimation, and a set of usage guidelines for developers when they incorporate a supervisor based on uncertainty monitoring into a deep-learning-based system. Key guidelines include the noncritical choice of which supervisor to use, the advantages offered by ensemble supervisors, when enough computational resources allow their adoption, and in general the importance of infield assessment and improvement of the adopted supervisors (recommended by Robert Hierons). The second paper, “On subsumption relationships in data flow testing” by Marcos Lordello Chaim, Kesina Baral, Jeff Offutt, Mario Concilio Neto, and Roberto Paulo Andrioli de Araujo, tackles the problem of reliably identifying subsumption for definition-use associations in data flow testing by modelling it as a data flow analysis framework and developing algorithms that correctly find data flow subsumptions and are asymptotically less costly than previous algorithms. The authors empirically investigate the effectiveness and scalability of the data flow subsumptions by applying data flow testing on programs. The experimental results show that data flow subsumption is effective at reducing the number of definition-use associations to be tested and can be found at scale (recommended by Robert Hierons). We hope that these papers will inspire further research in related directions.
PENGARUH PROGRAM PELATIHAN DASAR KOMPUTER TERHADAP KEMAMPUAN MENGOPERASIKAN MICROSOFT OFFICE DI SMP N 5 KUPANG TIMUR

One of the basic skills in the current digitalization era is the skill to operate a computer, especially operating Microsoft Office. The problems found in the field are the lack of school facilities in providing computers, lack of computer ownership by each student and limited learning time to deepen Microsoft Office practicum. One solution to overcome this is to hold a basic computer training program. Therefore, the purpose of this study was to determine the extent of the influence of the training program on computer operating skills. This research is a quantitative research with a type of experimental design. The sample in this study was 22 class VII students of SMP N 5 East Kupang. Data collection techniques were carried out by means of skills tests and non-tests (observation). The data analysis technique uses a prerequisite analysis test which consists of a normality test and a variance homogeneity test. Furthermore, using the N-Gain test to see the effect of basic computer training programs on students' skills in operating Microsoft Office. The results showed that the significance value of the paired samples test was 0.000 &lt;0.05, which means that there was an influence on the basic computer training program on the ability of students at SMP N 5 Kupang Timur in operating Microsoft Office. It can also be seen from the N-Gain score of 58.2% that the basic computer training program conducted was quite influential in improving the skills of SMP N 5 Kupang Timur students in operating Microsoft Office.
CREATING A BETTER YORUBA LANGUAGE LEARNING APP: LEVERAGING COLLECTIVE INTELLIGENCE AND EXPERIENCE

This project seeks to delve into the integration of fundamental concepts of collective intelligence to develop a more comprehensive Yoruba language learning application. The concept of collective intelligence, exemplified in James Surowiecki's "The Wisdom of Crowds," traces its roots to Francis Galton's demonstration at a fair where 800 participants estimated a cow's weight. This collective guess accurately predicted the weight, highlighting group intelligence's potential (Surowiecki, 2004). Groups outperforming individuals is a central theme; diverse teams located red balloons across the US in 9 hours and identified an elusive enzyme's structure in 3 weeks (Pickard et al., 2011; Khatib et al., 2011). Collective intelligence empowers groups to tackle complex issues; Yorùbá, a West African language, showcases this power. Spoken in Nigeria and beyond, it preserves knowledge and culture. Yorùbá's significance transcends borders (Clarke, K. M. 2004; Fabuni et al., 2005).
PENGGUNAAN BUKU CERITA FABEL DENGAN MODEL QUANTUM LEARNING DALAM PEMBELAJARAN MEMBACA PERMULAAN DI SEKOLAH DASAR

The present study investigates the low level of early reading skills among first-grade students at Oetona Public Elementary School, as evidenced by their difficulties in letter and syllable spelling as well as distinguishing letter forms. Additionally, students exhibit a lack of enthusiasm when reading sentences or short stories found in books or written by the teacher on the whiteboard. To address this issue, the utilization of Fable Storybooks with the Quantum Learning Model in early reading instruction for first-grade students at Oetona Public Elementary School was introduced, aiming to make a positive contribution to the development of students' early reading skills. The research employed a qualitative approach with a descriptive design. The findings of the study indicate that the implementation of Fable Storybooks with the Quantum Learning Model has a positive impact on students' early reading abilities. The majority of the students achieved a level of "excellent" in their early reading skills. The results and analysis demonstrate that the use of Fable Storybooks with the Quantum Learning Model contributes significantly to the improvement of students' early reading outcomes. Students displayed high enthusiasm and imagination during the learning process and demonstrated notable progress in their reading abilities
Construction of a gaze correction model for an on-screen agent: eye-turn, head-turn, and body-turn perspectives on a flat screen

Abstract An on-screen agent displayed on a flat screen encounters the problem of the gazing target not being correctly recognized by humans in real space. This is because the image displayed on the screen does not change when the movement of the observer changes the relative angle. Therefore, gaze correction that considers the characteristics of the screen is necessary to correctly present the gaze of the on-screen agent. Despite numerous studies, there exists no formulation of the perception of gaze of an on-screen agent on a flat screen. In this study, we attempted to construct a gaze correction model that derives the appropriate eye-/head-/body-turn angle for three gazing methods based on a gazing target and a confronted human: gazing by eye-turn, gazing by head-turn, and gazing by body-turn. We hypothesized that the gaze correction model of an on-screen agent would be expressed by the sum of the linear terms of the eye- and screen-turn angle for gazing by eye-turn, and by the addition of the interaction term of the head-/body-turn and screen-turn angles for gazing by head-/body-turn. We assessed the efficacy of the gaze correction model. By performing hierarchical multiple regression analysis on the experimental data, we have shown that our hypothesis regarding the gaze correction model is correct and identifies the model's regression coefficients. Consequently, we were able to construct a gaze correction model for an on-screen agent gazing by eye-turn, head-turn, and body-turn, and contributed technology for communication with gaze between an on-screen agent and humans.
An application of ontology driven machine learning model challenges for the classification of social media data: a systematic literature review

This systematic literature review aimed to explore the challenges and limitations of applying ontology driven machine learning models to the classification of social media data. Social media platforms generate a vast amount of data that requires automated and reliable classification to facilitate analysis and decision-making. Ontology driven machine learning models offer a promising approach to address this need by harnessing the power of both ontologies and machine learning algorithms to improve accuracy and efficiency. However, the application of such models to social media data classification poses unique challenges due to the complex and dynamic nature of social media data. To address this research gap, a systematic literature search was conducted, and 20 studies were included in the review. The findings of this review suggest that ontology driven machine learning models offer a promising approach to address the challenges of social media data classification. However, the existing literature highlights several challenges that need to be addressed, such as ontology development, feature selection, and model validation. Overall, the review provides insights into the current state of research on ontology driven machine learning models for social media data classification, identifies research gaps, and suggests directions for future investigation.
Could the Universe be a giant quantum computer?

Computational rules might describe the evolution of the cosmos better than the dynamical equations of physics — but only if they are given a quantum twist. Computational rules might describe the evolution of the cosmos better than the dynamical equations of physics — but only if they are given a quantum twist.
Development of Smart Apps Creator Application-Based Learning Media in Production Orale Élémentaire Course

This study aims to design and develop learning media in the Production Orale Élémentaire course using the Smart Apps Creator media. The research method used is the ADDIE Research and Development (R&amp;D) model proposed by Dick and Carry by involving five stages namely analysis, design, development, implementation, and evaluation. However, this research is limited to the development stage. The material validation results were 97% while the media validation results were 95%. The average percentage of scores obtained from the two experts is 96% very good category. So, Smart Apps Creator learning media is valid for use in Production Orale Élémentaire course. Keywords: Learning Media, Smart Apps Creator, Production Orale Élémentaire
Preventing Child Kidnaping at Home Using CCTV that Utilizes Face Recognition with You Only Look Once (YOLO) Algorithm

The rise of child abductions often occurs because of weak parental supervision and weak vigilance of strangers who intentionally or unintentionally approach children. Early warning when a child is approached by an unknown person can cancel the intention of the kidnapper. Indeed, CCTV can be used as a tool to thwart crime. Face Recognition is one of the best biometric recognition systems for identifying someone whose body or DNA has certain characteristics that distinguish one person from another. This research resulted in the right collaboration between CCTV which is equipped with the YOLOv8 algorithm which can detect a human object in real-time and also utilizes face recognition which can prevent child abduction by providing an early warning to kidnappers when approaching children. The results of the first test are the optimal combination using 3 cameras and the CNN method and the shooting time in the afternoon. This combination succeeded in providing an early warning in the form of a siren whose sound is issued with the help of a loudspeaker when a child is left alone and approached by an unknown person with the distance between the camera and the object to be detected as far as 1 meter to 3 meters which takes 15 seconds to 22 seconds to complete the detection process in an image. Then the results of further testing with 6 cameras using the LBPH method take between 17 seconds to 18 seconds to complete the detection process in an image, SVM takes 20 seconds to 42 seconds and CNN takes 17 seconds to 24 seconds.
ARTIFICIAL INTELLIGENCE AND AUTHORSHIP OF INTELLECTUAL PROPERTY

The appearance of the extremely powerful artificial intelligence chatbot “ChatGPT” in late 2022 has made urgent the finding of solutions to the questions of the authorship and protectability of works created with the aid of artificial intelligence. These questions are particularly acute in the areas of copyright and patent law where human creativity traditionally has been a requirement for protection. The present article discusses these issues in the light of experiments with ChatGPT.
The Evaluation of the S1 Students’ Frequency Use of Reading Comprehension Strategies and Familiarity with them: A Case Study at the Faculty of Languages, Literature and Art, Ibn Tofail University, Kenitra, Morocco

The most challenging skill for S1 university students in the EFL context is Reading comprehension. This may be due to the lack of possessing the necessary reading comprehension strategies that would help them to understand English texts efficiently. In this respect, this study is first intended to determine the relationship between the frequency of use of reading-comprehension strategies and the students’ familiarity with them before and after completing the usual curriculum of reading comprehension course. Furthermore, this study also attempts to evaluate the effect of the S1 students’ familiarity with and frequency of use of reading comprehension strategies on their performance in reading comprehension tasks. A sample of 85 University students in EFL context was chosen, but only 73 of them filled in the questionnaire and completed their pre-test and post-test. All of these 73 students attended the usual reading curriculum without any special treatment. The findings of the study indicate that the students’ awareness and frequency of use do not show any improvement as the S1 students completed their usual reading comprehension courses. Another major finding is that reading strategies frequency use and familiarity with them show no correlation with the students’ performance while taking both the pre-test and post-test. Therefore, the fact that S1 students show no development of both their use and awareness of reading comprehension strategies after completing the usual reading comprehension course and also no improvement in their performance can be explained by the ineffective training that they underwent during Semester 1 of their English studies journey. In other words, the students’ high or low scores in reading comprehension courses are not determined by their familiarity with reading comprehension strategies or their frequency use. Thus, researchers on education are urged to hold studies that shall investigate the teaching of reading comprehension strategies in the usual curriculum and how they should be taught efficiently.
Multi-Attention Multi-Image Super-Resolution Transformer (MAST) for Remote Sensing

Deep-learning-driven multi-image super-resolution (MISR) reconstruction techniques have significant application value in the field of aerospace remote sensing. In particular, Transformer-based models have shown outstanding performance in super-resolution tasks. However, current MISR models have some deficiencies in the application of multi-scale information and the modeling of the attention mechanism, leading to an insufficient utilization of complementary information in multiple images. In this context, we innovatively propose a Multi-Attention Multi-Image Super-Resolution Transformer (MAST), which involves improvements in two main aspects. Firstly, we present a Multi-Scale and Mixed Attention Block (MMAB). With its multi-scale structure, the network is able to extract image features from different scales to obtain more contextual information. Additionally, the introduction of mixed attention allows the network to fully explore high-frequency features of the images in both channel and spatial dimensions. Secondly, we propose a Collaborative Attention Fusion Block (CAFB). By incorporating channel attention into the self-attention layer of the Transformer, we aim to better establish global correlations between multiple images. To improve the network’s perception ability of local detailed features, we introduce a Residual Local Attention Block (RLAB). With the aforementioned improvements, our model can better extract and utilize non-redundant information, achieving a superior restoration effect that balances the global structure and local details of the image. The results from the comparative experiments reveal that our approach demonstrated a notable enhancement in cPSNR, with improvements of 0.91 dB and 0.81 dB observed in the NIR and RED bands of the PROBA-V dataset, respectively, in comparison to the existing state-of-the-art methods. Extensive experiments demonstrate that the method proposed in this paper can provide a valuable reference for solving multi-image super-resolution tasks for remote sensing.
Innovation Diffusion and Sustainable Development of Deposit Money Banks in South-South Nigeria: The Moderating Role of Entrepreneurial Marketing

Many deposit money banks in Nigeria are utilizing new technologies to innovate their products and services. But amidst these wins are some legitimate concerns about the credibility of the banks, customer trust, banks’ technical competence, capability to adopt innovations and the ability to sustain development. Framed by the diffusion of innovation (DOI) theory, this study explored the relationship between innovation diffusion and sustainable development of deposit money banks in Nigeria and the moderating role of entrepreneurial marketing. A quantitative and cross-sectional survey research design was adopted to consider three (3) hypotheses formulated for the study. Based on a population size of twenty-two (22) deposit money banks in south-south Nigeria, (Nigeria Deposit Insurance Corporation (NDIC), 2022), two (2) respondents; branch managers and marketing managers were drawn randomly from each of the banks for the survey. Therefore, forty-four (44) copies of questionnaire representing the total respondents were suitably designed and administered to obtain primary data. The face validity of the questionnaire was determined by experts comprising of researchers and business practitioners with satisfactory knowledge of the subject matter. The construct and content validity were established from earlier researchers who used it, but with few adjustments. The Cronbach's Alpha Coefficient reliability measure was also applied to determine the internal consistency of items in the questionnaire using Statistical Package for Social Sciences (SPSS). The Pearson moment correlation coefficient was used to determine the degree of relationship between the dimensions of innovation diffusion (product innovation diffusion and process innovation diffusion) and sustainable development. Partial correlation was used to examine moderation of entrepreneurial marketing on the relationship between innovation diffusion and sustainable development. Findings reveal that inn
Microcomb-Driven Optical Convolution for Car Plate Recognition

The great success of artificial intelligence (AI) calls for higher-performance computing accelerators, and optical neural networks (ONNs) with the advantages of high speed and low power consumption have become competitive candidates. However, most of the reported ONN architectures have demonstrated simple MNIST handwritten digit classification tasks due to relatively low precision. A microring resonator (MRR) weight bank can achieve a high-precision weight matrix and can increase computing density with the assistance of wavelength division multiplexing (WDM) technology offered by dissipative Kerr soliton (DKS) microcomb sources. Here, we implement a car plate recognition task based on an optical convolutional neural network (CNN). An integrated DKS microcomb was used to drive an MRR weight-bank-based photonic processor, and the computing precision of one optical convolution operation could reach 7 bits. The first convolutional layer was realized in the optical domain, and the remaining layers were performed in the electrical domain. Totally, the optoelectronic computing system (OCS) could achieve a comparable performance with a 64-bit digital computer for character classification. The error distribution obtained from the experiment was used to emulate the optical convolution operation of other layers. The probabilities of the softmax layer were slightly degraded, and the robustness of the CNN was reduced, but the recognition results were still acceptable. This work explores an MRR weight-bank-based OCS driven by a soliton microcomb to realize a real-life neural network task for the first time and provides a promising computational acceleration scheme for complex AI tasks.
Privatized graph federated learning

Abstract Federated learning is a semi-distributed algorithm, where a server communicates with multiple dispersed clients to learn a global model. The federated architecture is not robust and is sensitive to communication and computational overloads due to its one-master multi-client structure. It can also be subject to privacy attacks targeting personal information on the communication links. In this work, we introduce graph federated learning, which consists of multiple federated units connected by a graph. We then show how graph-homomorphic perturbations can be used to ensure the algorithm is differentially private on the server level. While on the client level, we show that improvement in the differentially private federated learning algorithm can be attained through the addition of random noise to the updates, as opposed to the models. We conduct both convergence and privacy theoretical analyses and illustrate performance by means of computer simulations.
Application of the Hamiltonian System in Deriving Solution to Dynamic System of the Sectoral Labour Market

The paper describes the application of the Hamiltonian System in deriving a solution to the dynamic system of the sectoral labour market. The aim is to determine the best control that would allow maximum sector participation in a three-sector labour market. The three sectors of the labour market are the goods-producing sector, the service-providing sector, and the agriculture sector. The sectoral labour market is formulated as an optimal control problem whose solution is sought using the Hamiltonian system by following the necessary conditions for optimality in the Hamiltonian System of Pontryagin’s Maximum Principle. The paper considered that the optimal control would be the measure of labour market efforts to produce the general worker’s population, and any split would mean there is production of active sector productive workers. The sector with the highest optimal allocation of effort, defined as the control within a fixed time, is identified as the best-performing sector. Service-providing sector, in this case, presented the best control. The obtained optimal control parameter is later applied to the experimental data to check on the effect of the control on the sectoral labour market participation rate. The results of the problem indicated that the application of the control had a significant effect on the sectoral labour market participation rate as compared to when there was no control. The sector with the highest annual growth rate measured as the sectoral participation rate was identified as the goods-producing sector