Reviewerly/exHarmony huggingface dataset:
Strengthening Reading Comprehension Skills Through Fluency-Oriented Reading Instruction (Fori)

Students who struggled in reading lacked the necessary skills and attention. This Action Research determined the efficacy of Fluency-Oriented Reading Instruction (FORI) in strengthening reading comprehension skills of students in English. It involved the Grade V students who were considered as struggling readers. It lasted for two weeks where learners were got involved in an hour reading activity every day after their classes. Likewise, thematic analysis was done in identifying the themes. Findings revealed that the intervention increased the reading comprehension skills of the students and even made them as independent readers. They suggested that teachers from all grade levels should have to adopt FORI and books would be available at home.
IMPROVING E-COMMERCE APPLICATION THROUGH SENSE OF AGENCY OF A CALIBRATED INTERACTIVE VR APPLICATION

Virtual Reality (VR) technologies create and control different virtual world instead of the actual environment, and this contributes to the feeling of control known as the sense of agency (SoA). The SoA exists from the contrast between the expected sensory consequence of one’s action from efference copy and the real sensory effects. However, the size representation of objects differs between the physical and virtual world due to certain technical limitations, such as the VR application’s virtual hand not reflecting the user’s actual hand size. A limitation that will incur low quality of perception and SoA for digital application. Here, we proposed a proof-of-concept of an interactive e-commerce application that incorporates VR capability and size calibration mechanism. The mechanism uses a calibration method based on the reciprocal scale factor from the virtual object to its real counterpart. The study of the SoA focusing on user perception and interaction was done. The proposed method was tested on twenty two participants − who are also online shopping users. Nearly half of the participants (45%) buy online products frequently, at least one transaction per day. The outcome indicates that our proposed method improves 47% of user perception and interaction compared to the conventional e-commerce application with its static texts and images. Our proposed method is rudimentary yet effective and can be easily implemented in any digital field.
Chatbots as a component of the content of teaching the basics of artificial intelligence at school

The article discusses the relevance of teaching schoolchildren the basics of artifcial intelligence. The importance of chatbots is emphasized as a component of the content of the theme "Fundamentals of artifcial intelligence" for the development of interdisciplinary links of the school course of informatics with other areas of knowledge. The theoretical aspects of teaching schoolchildren the technology of creating chatbots are considered: the characteristics of the basic concepts, historical information, the most common classifcation, the mechanism for processing user requests by the chatbot. Guidelines for teaching the technology of creating a chatbot are proposed. The designers for creating chatbots available for development by schoolchildren are listed. A plan of practical work "Creating a chatbot in the Aimylogic online constructor" is given. In order to illustrate the results of approbation of the educational material under consideration, examples of chatbots are given: a chatbot that talks about various types of algebraic equations; a chatbot — assistant for fractions; a chatbot — consultant on the theme "Algorithm and its properties. Ways of algorithm representation". The presented examples include the structure of the chatbot dialog created in the Aimylogic constructor and in the Pencil Code service.
The system of differentiated tasks for the formation of universal learning activities for students on the theme "databases. search for information"

The article discusses aspects of the formation of universal learning activities for students on the theme "Databases. Search for information" using diﬀerentiated tasks. The use of diﬀerentiated tasks makes it possible to ensure the implementation of a student-centered approach to learning within the framework of the traditional class-lesson system, taking into account the interests and needs of students. It is the variability of diﬀerentiated tasks that will ensure the formation of universal learning activities for each student. On the basis of thematic planning by K. Yu. Polyakov, E. A. Eremin three practical and one control works have been developed. The works contain tasks for three groups of students: with a low level of learning opportunities, with a high level of learning opportunities, and others. When solving tasks, students will be supported by positive motivation, personal growth will be ensured, a desire for selfrealization will be formed, which will ultimately ensure the formation of universal learning activities.
Digital transformation of mathematical education as overcoming the Digital gap (on the example of teaching stochastics)

The article sequentially examines the content of the terms: computerization of education, informatization of education, digitalization of education, and digital transformation of education. The essence of digital transformation in the feld of mathematics education is concretized. It is proposed to defne digital transformation of mathematics education as overcoming the new digital divide, carried out by eliminating the inequality in the use of digital technologies by the subjects of the educational process. Elimination of such inequality, from our point of view, is possible thanks to the development and implementation of eﬀective digital teaching materials and technologies in the school's practice. Among the technologies we have highlighted are: artifcial intelligence, the Internet of things, additive manufacturing, machine learning, blockchain, virtual and augmented reality. We have described the possibilities of implementing these technologies using the example of teaching schoolchildren to stochastics, one of the branches of mathematics.
Cybercrime in Assessment

This study investigated cybercrime in Assessment among students in Rivers State University, Port Harcourt. Two research questions and two corresponding null hypotheses guided the study. Descriptive survey research design was adopted for the study. A sample of 400 year III students were drawn from the population through stratified proportionate sampling technique and used for the study. An instrument titled “Cybercrime Assessment Scale” (CAS) was used for the data analysis. The Face and Content validities of the instrument were determined by experts in measurement and evaluation. Test-retest method was used to determine the validity of the instrument and the coefficient obtained for the CAS was 0.77. Mean, standard deviation and independent t-test was used for the data analysis. The findings shows that; gender, Age significantly influence cybercrime in assessment among students in Rivers State university independently taken. Based on the findings conclusion and recommendations were made.
Efficient configuration of optimization algorithms

We propose a set of capping methods to speed-up the automatic configuration of optimization algorithms. First, we build a performance envelope based on previous executions of known configurations, which defines the minimum required performance for new configurations. Then, we use the performance envelope to evaluate new configurations, stopping poor performers early. We propose different methods to aggregate previous executions into a performance envelope, and evaluate them on several configuration scenarios. The proposed methods produce solutions of the same or better quality as configuring without capping, but reduce the effort required to configure optimization algorithms.
Measuring the ability of lexicase selection to find obscure pathways to optimality

This Hot-off-the-Press paper summarizes our recently published work, "An Exploration of Exploration: Measuring the Ability of Lexicase Selection to Find Obscure Pathways to Optimality," published as a chapter in Genetic Programming Theory and Practice XVIII [3]. In evolutionary search, selection schemes drive populations through a problem's search space, often trading off exploitation with exploration. Indeed, problem-solving success depends on how a selection scheme balances search space exploitation with exploration. In [3], we introduce an "exploration diagnostic" that measures a selection scheme's ability to explore different pathways in a search space. We use our exploration diagnostic to investigate the exploratory capacity of lexicase selection and several of its variants: epsilon lexicase, down-sampled lexicase, cohort lexicase, and novelty lexicase. We verify that lexicase selection out-explores tournament selection, and we demonstrate that lexicase selection's ability to explore a search space is sensitive to the ratio between population size and the number of test cases used for evaluating candidate solutions. We find that relaxing lexicase selection's elitism with epsilon lexicase can further improve search space exploration. Additionally, we find that both down-sampled and cohort lexicase---two methods of applying random subsampling to test cases---substantially degrade lexicase's exploratory capacity; however, cohort partitioning better preserves exploration than down-sampling. Finally, we find evidence that the addition of novelty-based test cases can degrade lexicase selection's exploratory capacity.
Amaru

We present Amaru, a framework for Genetic Improvement utilizing Abstract Syntax Trees directly at the interpreter and compiler level. Amaru also enables the mining of frequent, discriminative patterns from Genetic Improvement populations. These patterns in turn can be used to improve the crossover and mutation operators to increase population diversity, reduce the number of individuals failing at run-time and increasing the amount of successful individuals in the population.
Transfer learning in evolutionary spaces

tutorial Open Access Share on Transfer learning in evolutionary spaces Author: Nelishia Pillay University of Pretoria, South Africa University of Pretoria, South AfricaView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1038–1045https://doi.org/10.1145/3520304.3533632Published:19 July 2022Publication History 0citation34DownloadsMetricsTotal Citations0Total Downloads34Last 12 Months34Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Model-based evolutionary algorithms

tutorial Share on Model-based evolutionary algorithms: GECCO 2022 tutorial Authors: Dirk Thierens Utrecht University, Utrecht, The Netherlands Utrecht University, Utrecht, The NetherlandsView Profile , Peter A. N. Bosman CWI, Amsterdam, The Netherlands CWI, Amsterdam, The NetherlandsView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1141–1170https://doi.org/10.1145/3520304.3533645Published:19 July 2022Publication History 0citation29DownloadsMetricsTotal Citations0Total Downloads29Last 12 Months29Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
IOHanalyzer: Detailed performance analyses for iterative optimization heuristics

This paper summarizes our work "IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics", to appear in ACM Transactions on Evolutionary Learning and Optimization.
Benchmarking and analyzing iterative optimization heuristics with IOH profiler

tutorial Open Access Share on Benchmarking and analyzing iterative optimization heuristics with IOH profiler Authors: Carola Doerr View Profile , Hao Wang View Profile , Diederick Vermetten View Profile , Thomas Bäck View Profile , Jacob de Nobel View Profile , Furong Ye View Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 1334–1341https://doi.org/10.1145/3520304.3533665Published:19 July 2022Publication History 0citation34DownloadsMetricsTotal Citations0Total Downloads34Last 12 Months34Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Modular grammatical evolution for the generation of artificial neural networks

This paper proposes a NeuroEvolution algorithm, Modular Grammatical Evolution (MGE), that enables the evolution of both topology and weights of neural networks for more challenging classification benchmarks like MNIST and Letter with 10 and 26 class counts. The success of MGE is mainly due to (1) restricting the solution space to regular network topologies with a special form of modularity, and (2) improving the search properties of state-of-the-art GE methods by improving the mapping locality and the representation scalability. We have defined and evaluated five forms of structural constraints and observe that single-layer modular restriction of solution space helps in finding smaller and more efficient neural networks faster. Our experimental evaluations on ten well-known classification benchmarks demonstrate that MGE-generated neural networks provide better classification accuracy with respect to other NeuroEvolution methods. Finally our experimental results indicate that MGE outperforms other GE methods in terms of locality and scalability properties.
Benchmarking several strategies to update the penalty parameters in AL-CMA-ES on the bbob-constrained testbed

In this paper, we benchmark several versions of a population-based evolution strategy with covariance matrix adaptation, handling constraints with an Augmented Lagrangian fitness function. The versions only differ in the strategy to adapt the penalty parameter of the fitness function. We compare the resulting algorithms, AL-CMA-ES, with random search and Powell's derivative-free COBYLA on the recently released bbob-constrained test suite for constrained continuous optimization in dimensions ranging from 2 to 40. The experimental results allow identifying classes of problems where one algorithm is more advantageous to use. They also reveal features of the merit function used for performance assessment and in particular situations where even on simple problems the targets can be hard to meet for algorithms based on Lagrange multipliers.
Long-term evolution experiment with genetic programming [hot of the press]

We evolve floating point Sextic polynomial populations of genetic programming binary trees for up to a million generations. We observe continued innovation but this is limited by their depth and suggest deep expressions are resilient to learning as they disperse information, impeding evolvability and the adaptation of highly nested organisms and instead we argue for open complexity. Programs with more than 2 000 000 000 instructions (depth 20 000) are created by crossover. To support unbounded long-term evolution experiments LTEE in GP we use incremental fitness evaluation and both SIMD parallel AVX 512 bit instructions and 16 threads to yield performance equivalent of up to 1.1 trillion GP operations per second, 1.1 tera-GPops, on an Intel Xeon Gold 6136 CPU 3.00GHz server.
EDUCATORS' INFORMATION AND COMMUNICATION TECHNOLOGY KNOWLEDGE, ATTITUDES, AND USE:THEIR EFFECT ON SCHOOL PERFORMANCE

The study aimed to determine the level of Department of Education, Division of Laguna secondary school educators' information and communication technology (ICT) knowledge, attitudes, and use and its effects on schools' performance.The respondents were the sixteen schools and 32 teachers from Laguna SBM Level III public secondary schools.The researcher used the descriptive research design to gather the necessary data and information about the relationship between the independent variable, i.e., teachers' ICT knowledge, attitude, and use, and the dependent variable, i.e., the schools' performance and SBM assessment.The primary data gathering instrument was the survey questionnaire which the ICT coordinators in the Division of Laguna validated.Statistical data treatments of the descriptive part were frequency count, mean, and standard deviation, whereas, for the inferential part, the Pearson product-moment of correlation was used.The study revealed that the teachers' ICT knowledge was high based on the five sub-variables -cultural, cognitive, constructive, communication, and creative.Their ICT attitudes based on three aspects -emotional effect, personal interest, and subjective satisfaction, as well as their ICT utilization based on four aspects -personal development, professional development, other educational stakeholders' aspect, and the aspect of advocacy, were also high, which conveyed positive.The schools' performance based on performance improvement consisting of enrolment increase rate, promotion rate, and high achievement rate was high.All the No significant relationship was found between the teachers' ICT knowledge, attitude, and use and school performance improvement; therefore, the hypothesis was accepted.Relatively, no significant relationship between the teachers' ICT knowledge, attitude, and use and the school performance based on the SBM assessment was recorded except for the cognitive aspect in ICT knowledge which the hypothesis was partially accepted.
Vertically Distributed Computing Technology

We developed a technology that quickly shares information found by a connected vehicle with other vehicles.For example, when a connected vehicle finds an obstacle on the road, the technology can transmit information about it quickly to other vehicles.This quick notification is achieved by offloading part of the processing of the collected obstacle information to network-edge nodes and transmitting interim results to vehicles near the obstacle.However, in urban areas, the delay in notification may become large because a large number of vehicles connect to a small number of network-edge nodes; thus, the volume of data received from these vehicles may overwhelm the computing resources of these nodes.To solve this problem, we also developed a technology that dynamically selects which computer will execute any particular notification processing on the basis of the states of the connected vehicles.Using this technology, obstacle information can be transmitted quickly even when a large number of vehicles are connected to a small number of network-edge nodes.
ASSISTING THE TEACHING OF MATHEMATICS IN PRIMARY SCHOOLS USING INTELLIGENT SOFTWARE

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 5096-5102ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1215Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
Factors Affecting the Level of Employee Performance of AA Manufacturing

The purpose of this paper is to determine if a firm's environment factors, job-related factors, and employee-related factors affect employee performance.Using a descriptive and causal research design, AA employees were asked to complete an online survey using the questionnaire adopted from Diamantidis and Chatzoglou (2018).The mean was used to summarize the respondents' perceptions of the variables, while simple and multiple linear regression were used to test the hypotheses formulated.According to the findings, job-related and employee-related factors have a significant effect on employee performance, whereas firm environment factors have no significant effect on employee performance.Furthermore, firm environment and job-related factors significantly affect employee-related factors, whereas firm environment has a significant effect on job-related factors.The findings will serve as a starting point to reexamine AA Manufacturing's traditional work policies and expectations, allowing researchers to come up with a CAPSTONE Project that can be discussed with executives.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double Anonymous • Conference submission management system: Morressier • Number of submissions received: 86 • Number of submissions sent for review: 86 • Number of submissions accepted: 30 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 35% • Average number of reviews per paper: 1.00 • Total number of reviewers involved: 42 • Contact person for queries: Name: Linda Ye Affiliation: The International Society for Applied Computing (ISAC) Email: linda.ye@applied-computing.net
Learning 3D Information from 2D Images Using Aperture Rendering Generative Adversarial Networks toward Developing a Computer that “Understands the 3D World”

When people look at photos, they can estimate three-dimensional (3D) information, such as depth, from their experience and knowledge, but computers have difficulty in doing so because they cannot have such experience and knowledge.We spoke to Takuhiro Kaneko, a distinguished researcher who developed a novel deep learning model that can learn 3D information from standard 2D images such as those on the web.