Search results for: televised elementary school learning

Authors: Marrone Silverio Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner, Djamel Fawzi Hadj Sadok

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The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981.

Keywords: RJ45, automatic annotation, object tracking, 3D projection

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Authors: Marina Tsetseli, Eleni Zetou, Maria Michalopoulou, Nikos Vernadakis

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Game performance, besides the accuracy and the quality skills execution, depends heavily on where the athletes will focus their attention while performing a skill. The purpose of the present study was to examine and compare the effect of internal and external focus of attention instructions on the decision making in tennis at players 8-9 years old (M=8.4, SD=0.49). The participants (N=40) were divided into two groups and followed an intervention training program that lasted 4 weeks; first group (N=20) under internal focus of attention instructions and the second group (N=20) under external focus of attention instructions. Three measurements took place (pre-test, post-test, and retention test) in which the participants were video recorded while playing matches in real scoring conditions. GPAI (Game Performance Assessment Instrument) was used to evaluate decision making in three game situations; service, return of the service, baseline game. ANOVA repeated measures (2 groups x 3 measurements) revealed a significant interaction between groups and measurements. Specifically, the data analysis showed superiority of the group that was instructed to focus externally. The high scores of the external attention group were maintained at the same level at the third measurement as well, which indicates that the impact was concerning not only performance but also learning. Thus, cues that lead to an external focus of attention enhance the decision-making skill and therefore the game performance of the young tennis players.

Keywords: decision making, evaluation, focus of attention, game performance, tennis

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Authors: Mishack Thiza Gumbo

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The participatory action research study reported in this paper aims to explore indigenous mathematics, science, and technology to develop an indigenous Mathematics, Science and Technology Education Master’s Programme ultimately. The study is based on an ongoing collaborative project between the Mathematics, Science and Technology Education Departments of the University of South Africa, University of Botswana and Chinhoyi University of Technology. The study targets the Mathematics, Science and Technology Education Master’s students and indigenous knowledge holders in these three contexts as research participants. They will be interviewed; documents of existing Mathematics, Science and Technology Education Master’s Programmes will be analysed; mathematics, science and technology-related artefacts will also be collected and analysed. Mathematics, Science, and Technology Education are traditionally referred to as gateway subjects because the world economy revolves around them. Scores of scholars call for the indigenisation of research and methodologies so that research can suit and advance indigenous knowledge and sustainable development. There are ethnomathematics, ethnoscience and ethnotechnology which exist in indigenous contexts such as blacksmithing, woodcarving, textile-weaving and dyeing, but the current curricula and research in institutions of learning reflect the Western notions of these subjects. Indigenisation of the academic programmecontributes toward the decolonisation of education. Hence, the development of an indigenous Mathematics, Science and Technology Education Master’s Programme, which will be jointly offered by the three universities mentioned above, will contribute to the transformation of higher education in this sense.

Keywords: indigenous, mathematics, science, technology, master's program, universities, collaboration

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Authors: Madjid Nancy, Rakinaung Natalia, Lumowa Fresy

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Background: Bullying becomes one of the problems that concern the world of education, especially in adolescents, which has a negative impact on learning achievement, psychology, and physical health. The psychological impact is shame, depression, distress, fear, sadness, and anxiety, so that if prolonged leave can lead to depression in the victim. While the impact on physical health in the form of bruises on the hit area, blisters, swelling and in more severe cases will lead to death. Objectives: This study aims to explore the perception of bullying in adolescent students Sekolah Menengah Kejuruan (SMK) Negeri 1 Manado and the people associated with that adolescent students. Methods: This research uses descriptive qualitative research design and using thematic analysis, and supported by Urie Bronfenbrenner Ecological Framework. The data collection that will be used is by in-depth interview. Sampling using purposive sampling and snowball techniques. This research was conducted at SMK Negeri 1 Manado. Result: From the analysis obtained three themes with the categories: 1) the perception of bullying with categories are: Understanding of Bullying and The Impact of Bullying, 2) the originator of bullying with categories are: Fulfillment of Youth Development Tasks and Needs, Peers Influence, and Family Communication; 3) the effort to handle bullying with categories are: the Individual Coping and Teacher Role. Conclusion: This research get three themes, those are perception of bullying, bullying’s originator and the effort of handling bullying.

Keywords: adolscent, students, bullying, perception

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Authors: Rajae Tahri, Omar Chokairi, Asmae Saadi, Souad Chaouir

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Background: The factors motivating students to choose a medical career is a long-standing topic of publication and discussion. To our knowledge, no national study on the motivation for choosing medical studies has been published to date. Population and methods: This is an observational, descriptive, and cross-sectional study of first-year medical students at the Faculty of Medicine and Pharmacy of Rabat. An anonymous questionnaire comprising 16 questions was developed and distributed to students during Embryology tutorials. The students were free to fill it in or not. The number of students who consented to participate in the survey was 266. The variables studied are the socio-demographic variables of the students and the reasons for choosing medical studies. Results: The most strongly and frequently chosen reasons for choice by our students were saving lives (64.9%), helping others (62.1%), love of medicine (57%), and reducing suffering (56.5%). The comparison of the results according to gender showed a significant difference between the degree of self-motivation of girls compared to that of boys (p <0.001). The reason that stood out the most for them was teamwork. The presence of a health professional in the family was associated with strong extrinsic motivation (p = 0.005). Conclusion: Understanding medical student career choices would improve our knowledge of the factors that influence medical student learning and performance. This knowledge will make it possible to adapt the educational strategies to maintain the motivation of the students throughout their course as well as during their exercise.

Keywords: motivation, motivational profiles, medical studies, Morocco

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Authors: Mayuko Matsuoka

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This presentation reports the relationships among EFL(English as a Foreign Language) students’ story comprehension in reading a story written in English and Japanese and empathic reactions. The main focus is put on their self-reflection on past study experiences, one of the empathic reactions after reading a story. One hundred fifty-five first-year university students in Japan read three SRL-themed stories written in English (their foreign language) and those written in Japanese (their mother tongue). The levels of the stories are equivalent, at CEFR(Common European Framework of Reference for Languages) B2 level. The result of categorical correlation analysis shows significant moderate correlations among three empathic reactions in a group reading English versions: having similar emotions as a protagonist, reflecting on their past study experiences, and getting lessons from a story. In addition, the result of logistic regression analysis for the data in a group reading English versions shows the chance of getting lessons from a story significantly approximately doubles if participants’ scores of a comprehension test increases by one, while it approximately triples if participants’ self-reflection occurs. These results do not appear in a group reading Japanese versions. The findings imply that self-reflection may support their comprehension of the English texts and leads to the participants’ getting lessons about SRL.

Keywords: comprehension, lesson, self-reflection, SRL

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Authors: Olumuyiwa Ojo, Masengo Ilunga

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Artificial neural network (ANN) has broken the bounds of the convention programming, which is actually a function of garbage in garbage out by its ability to mimic the human brain. Its ability to adopt, adapt, adjust, evaluate, learn and recognize the relationship, behavior, and pattern of a series of data set administered to it, is tailored after the human reasoning and learning mechanism. Thus, the study aimed at modeling wastewater treatment process in order to accurately diagnose water control problems for effective treatment. For this study, a stage ANN model development and evaluation methodology were employed. The source data analysis stage involved a statistical analysis of the data used in modeling in the model development stage, candidate ANN architecture development and then evaluated using a historical data set. The model was developed using historical data obtained from Daspoort Wastewater Treatment plant South Africa. The resultant designed dimensions and model for wastewater treatment plant provided good results. Parameters considered were temperature, pH value, colour, turbidity, amount of solids and acidity. Others are total hardness, Ca hardness, Mg hardness, and chloride. This enables the ANN to handle and represent more complex problems that conventional programming is incapable of performing.

Keywords: ANN, artificial neural network, wastewater treatment, model, development

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Authors: Abdul Mannan Akhtar

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In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

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Authors: Emmanuel Ogala

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Cloud computing has been envisioned as the next-generation architecture of Information Technology (IT) enterprise. In contrast to traditional solutions where IT services are under physical, logical and personnel controls, cloud computing moves the application software and databases to the large data centres, where the management of the data and services may not be fully trustworthy. This is due to the fact that the systems are opened to the whole world and as people tries to have access into the system, many people also are there trying day-in day-out on having unauthorized access into the system. This research contributes to the improvement of cloud computing security for better operation. The work is motivated by two problems: first, the observed easy access to cloud computing resources and complexity of attacks to vital cloud computing data system NIC requires that dynamic security mechanism evolves to stay capable of preventing illegitimate access. Second; lack of good methodology for performance test and evaluation of biometric security algorithms for securing records in cloud computing environment. The aim of this research was to evaluate the performance of an integrated security system (ISS) for securing exams records in cloud computing environment. In this research, we designed and implemented an ISS consisting of three security mechanisms of biometric (fingerprint), auto-PIN and password into one stream of access control and used for securing examination records in Kogi State University, Anyigba. Conclusively, the system we built has been able to overcome guessing abilities of hackers who guesses people password or pin. We are certain about this because the added security system (fingerprint) needs the presence of the user of the software before a login access can be granted. This is based on the placement of his finger on the fingerprint biometrics scanner for capturing and verification purpose for user’s authenticity confirmation. The study adopted the conceptual of quantitative design. Object oriented and design methodology was adopted. In the analysis and design, PHP, HTML5, CSS, Visual Studio Java Script, and web 2.0 technologies were used to implement the model of ISS for cloud computing environment. Note; PHP, HTML5, CSS were used in conjunction with visual Studio front end engine design tools and MySQL + Access 7.0 were used for the backend engine and Java Script was used for object arrangement and also validation of user input for security check. Finally, the performance of the developed framework was evaluated by comparing with two other existing security systems (Auto-PIN and password) within the school and the results showed that the developed approach (fingerprint) allows overcoming the two main weaknesses of the existing systems and will work perfectly well if fully implemented.

Keywords: performance evaluation, fingerprint, auto-pin, password-based, security systems, cloud computing environment

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Authors: Mbah Fidelix Njong, Sabi Emile Forkwa

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Social cognition is an aspect of social psychology that focuses on how people process, store and apply information about others and social situations. It lay emphasis on how cognitive processes play in our social interactions. In this article, we try to show how culture can influence our ways of thinking about others, how we feel and interact with the world around us. Social cognitive processes involve perceiving people and how we learn about the people around us. It concerns the mental processes of remembering, thinking and attending to other people with different cultural backgrounds and how we attend to certain information about the world. Especially in an educational setting, students’ learning processes are most often than not influenced by their cultural background. We can also talk of social schemas. That’s people’s mental representation of social patterns and norms. This involves information about the societal role and the expectations of individuals within a group. These cognitive processes can also be influence by culture. There are important cultural differences in social cognition. In any social situation, two individuals may have different interpretations. Each person brings in a unique background of experiences, knowledge, social influence, feelings and cultural variations. Cultural differences can also affect how people interpret social situations. The same social behavior in one cultural setting might have completely different meaning and interpretation if observed or applied in another culture. However, as people interpret behaviors and bring out meaning from the interpretations, they act based on their beliefs about situations they are confronted with. This helps to reinforce and reproduce the cultural norms that influence their social cognition.

Keywords: social cognition, social schema, cultural influence, psychology

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Authors: Abdullahi Garba

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The study investigated the effects of practical activities on performance among biology students in Zaria education zone, Kaduna State, Nigeria. The population consists of 18 public schools in the Zaria Education Zone with a total number of 4,763 students. A random sample of 115 students was selected from the population in the study area. The study design was quasi-experimental, which adopted the pre-test, post-test experimental, and control group design. The experimental group was exposed to practical activities, while the control group was taught with the lecture method. A validated instrument, a biology performance test (BPT) with a reliability coefficient of 0.82, was used to gather data which were analyzed using a t-test and paired sample t-test. Two research questions and hypotheses guided the study. The hypotheses were tested at p≤0.05 level of significance. Findings revealed that: there was a significant difference in the academic performance of students exposed to practical activities compared to their counterparts; there was no significant difference in performance between male and female Biology students exposed to practical activities. The recommendation given was that practical activities should be encouraged in the teaching and learning of Biology for better understanding. The Federal and State Ministry of Education should sponsor biology teachers for training and retraining of teachers to improve the academic performance of students in the subject.

Keywords: biology, practical, activity, performance

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Authors: Rosa Buxeda, Lorenzo Saliceti-Piazza, Rodolfo J. Romañach, Luis Ríos, Sandra L. Maldonado-Ramírez

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Biopharmaceuticals manufacturing is one of the major economic activities worldwide. Ninety-three percent of the workforce in a biomanufacturing environment concentrates in production-related areas. As a result, strategic collaborations between industry and academia are crucial to ensure the availability of knowledgeable workforce needed in an economic region to become competitive in biomanufacturing. In the past decade, our institution has been a key strategic partner with multinational biotechnology companies in supplying science and engineering graduates in the field of industrial biotechnology. Initiatives addressing all levels of the educational pipeline, from K-12 to college to continued education for company employees have been established along a ten-year span. The Amgen BioTalents Program was designed to provide undergraduate science and engineering students with training in biomanufacturing. The areas targeted by this educational program enhance their academic development, since these topics are not part of their traditional science and engineering curricula. The educational curriculum involved the process of producing a biomolecule from the genetic engineering of cells to the production of an especially targeted polypeptide, protein expression and purification, to quality control, and validation. This paper will report and describe the implementation details and outcomes of the first sessions of the program.

Keywords: biomanufacturing curriculum, interdisciplinary learning, workforce development, industry-academia partnering

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Authors: Abhimanyu Pati, Krishna Kumar Veluri

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The paper intends to bring out a real life experience encountered during actual implementation of a large scale Tier-1 Enterprise Resource Planning (ERP) system in a multi-location, discrete manufacturing organization in India, involved in manufacturing of auto components and aggregates. The business complexities, prior to the implementation of ERP, include multi-product with hierarchical product structures, geographically distributed multiple plant locations with disparate business practices, lack of inter-plant broadband connectivity, existence of disparate legacy applications for different business functions, and non-standardized codifications of products, machines, employees, and accounts apart from others. On the other hand, the manufacturing environment consisted of processes like Assemble-to-Order (ATO), Make-to-Stock (MTS), and Engineer-to-Order (ETO) with a mix of discrete and process operations. The paper has highlighted various business plan areas and concerns, prior to the implementation, with specific focus on strategic issues and objectives. Subsequently, it has dealt with the complete process of ERP implementation, starting from strategic planning, project planning, resource mobilization, and finally, the program execution. The step-by-step process provides a very good learning opportunity about the implementation methodology. At the end, various organizational challenges and lessons emerged, which will act as guidelines and checklist for organizations to successfully align and implement ERP and achieve their business objectives.

Keywords: ERP, ATO, MTS, ETO, discrete manufacturing, strategic planning

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Authors: Shilpa Vasavada

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One of the 17 SustainableDevelopmentGoals focuses on gender equality. The paper is based on the learning derived from working with two Corporate Social Responsibility cases in India: one, CSR of an International Corporate and the other, CSR of a multi state national level corporate -on their efforts to integrate gender perspective in their agriculture and livestock based rural livelihood programs. The author tries to dissect how ‘gender equality’ is seen by these two CSRs, where the goals are different. The implications of a CSR’sunderstandingon ‘gender equality’ as a goal; versus CSR’s understanding of working 'with women for enhancing quantity or quality of production’ gets reflected in their orientation to staff, resource allocation, strategic level and in processes followed at the rural grassroots level. The paper comes up with examples of changes made at programmatic front when CSR understands and works with the focus on gender equality as a goal. On the other hand, the paper also explores the differential, at times, the negative impact on women and the programmes;- when the goals differ. The paper concludes with recommendations for CSRs to take up at their resource allocation and strategic level if gender equality is the goal- which has direct implication at their grassroots programmatic work. The author argues that if gender equality has to be implemented actually in spirit by a CSR, it requires change in mindset and thus an openness to changes in strategies and resource allocation pattern of the CSR and not simply adding on women in the way intervention has been going on.

Keywords: gender equality, approaches, differential impact, resource allocation

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Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

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Authors: S. Cobos-Guzman

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This paper presents the results of a mechatronic design based on a 4-wheel omnidirectional mobile robot that can be used in indoor logistic applications. The low-level control has been selected using two open-source hardware (Raspberry Pi 3 Model B+ and Arduino Mega 2560) that control four industrial motors, four ultrasound sensors, four optical encoders, a vision system of two cameras, and a Hokuyo URG-04LX-UG01 laser scanner. Moreover, the system is powered with a lithium battery that can supply 24 V DC and a maximum current-hour of 20Ah.The Robot Operating System (ROS) has been implemented in the Raspberry Pi and the performance is evaluated with the selection of the sensors and hardware selected. The mechatronic system is evaluated and proposed safe modes of power distribution for controlling all the electronic devices based on different tests. Therefore, based on different performance results, some recommendations are indicated for using the Raspberry Pi and Arduino in terms of power, communication, and distribution of control for different devices. According to these recommendations, the selection of sensors is distributed in both real-time controllers (Arduino and Raspberry Pi). On the other hand, the drivers of the cameras have been implemented in Linux and a python program has been implemented to access the cameras. These cameras will be used for implementing a deep learning algorithm to recognize people and objects. In this way, the level of intelligence can be increased in combination with the maps that can be obtained from the laser scanner.

Keywords: autonomous, indoor robot, mechatronic, omnidirectional robot

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Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Naveed Ali

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Under the construct of the ‘traditional family’ set-up (male and female parent) in the United Kingdom, the absence of a male parental figure remains a critical factor associated with an elevated risk of criminal behavior among youths. Empirical evidence suggests that father absence significantly correlates with increased rates of juvenile delinquency and criminality. For instance, data reveals that approximately 63% of young offenders in the United Kingdom originate from single-parent households, predominantly those without a father. Moreover, research displays that boys from father-absent homes are three times more likely to exhibit antisocial behavior compared to their peers from two-parent families. This absence can negatively impact educational attainment, with children from fatherless homes being twice as likely to leave school prematurely, thereby increasing their vulnerability to peer influence and gang affiliation- key pathways into criminal activities. Both legal frameworks and social policies in the United Kingdom acknowledge the pivotal role of family stability in crime prevention. Initiatives including parenting support programs, community-based interventions, and targeted youth services seek to address the challenges faced by single-parent families and mitigate the criminogenic effects of father absence. Despite these efforts, persistent challenges remain, including the need to address the broader socioeconomic determinants of family instability and to refine legal strategies that effectively address the root causes of youth offending linked to the absence of a male parental figure. A nuanced understanding of these dynamics is essential for developing more effective legal and social interventions aimed at reducing juvenile delinquency and supporting at-risk populations within the United Kingdom. This paper will highlight the significant impact of the absence of a male parental figure on youth crime rates in the United Kingdom, underlining the need for enhanced legal and social responses. By examining the interplay between family structure and juvenile offending, the paper will underline the importance of developing more comprehensive interventions that address both familial factors and the wider socioeconomic context. The findings aim to guide policymakers and practitioners in creating more effective strategies to reduce youth crime, ultimately strengthening support systems for vulnerable families and mitigating the adverse effects of father absence on young individuals.

Keywords: criminality, family law, legal framework, the united kingdom perspective

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Authors: Misbah Javaid, Vladimir Estivill-Castro, Rene Hexel

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The field of robotics is advancing rapidly to the point where robots have become an integral part of the modern society. These robots collaborate and contribute productively with humans and compensate some shortcomings from human abilities and complement them with their skills. Effective teamwork of humans and robots demands to investigate the critical issue of trust. The field of human-computer interaction (HCI) has already examined trust humans place in technical systems mostly on issues like reliability and accuracy of performance. Early work in the area of expert systems suggested that automatic generation of explanations improved trust and acceptability of these systems. In this work, we augmented a robot with the user-invoked explanation generation proficiency. To measure explanations effect on human’s level of trust, we collected subjective survey measures and behavioral data in a human-robot team task into an interactive, adversarial and partial information environment. The results showed that with the explanation capability humans not only understand and recognize robot as an expert team partner. But, it was also observed that human's learning and human-robot team performance also significantly improved because of the meaningful interaction with the robot in the human-robot team. Moreover, by observing distinctive outcomes, we expect our research outcomes will also provide insights into further improvement of human-robot trustworthy relationships.

Keywords: explanation interface, adversaries, partial observability, trust building

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

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Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: stacking, multi-layers, ensemble, multi-class

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Authors: Muftahu Jibirin Salihu, Hazri Jamil

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The persisting challenge of access and enrolment to higher education in commonwealth countries has been reported in several studies, including reports of the international organization such as World Bank, UNESCO among others however from the macro perspective. The overarching aim of this study is to examine alternative policy practices towards improving access to university education in Nigeria at meso level of policy practice from evidence base policy studies using one university as a case. The study adopted a qualitative approach to gain insightful understanding on the issue of the study employing a semi-structure interview and policy documents as the means for obtaining the data and other relevant information for the study. The participants of the study were purposively chosen which comprise of a number of individuals from the selected university and other related organization which responsible for the policies development and implementation of Nigerian higher education system. From the findings of the study, several initiatives have been taken at meso level to address this challenge including the introduction of the University Matriculation Program as an alternative route for enhancing to access to the university education. However, the study further provided a number of recommendations which aimed at improving access to university education such as improving the entry requirements, society orientation on university education and the issue of ranking of certificate among the Nigerian higher institutions of learning.

Keywords: policy practice, access, enrolment, university, education, Nigeria

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Authors: Maquiling Jhuvie Jane R., Ojastro Regine B., Peroja Loreille Marie B., Pinili Joy Angela., Salve Genial Gail M., Villavicencio Marielle Irene B., Yap Alther Francis Garth B.

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Introduction: This study investigated the impact of prolonged device usage on wrist pain and perceived academic performance among college students in Computer Studies. The research aims to explore the correlation between the frequency of technological device use and the incidence of wrist pain, as well as how this pain affects students' academic performance. The study seeks to provide insights that could inform interventions to promote better musculoskeletal health among students engaged in intensive technology use to further improve their academic performance. Method: The study utilized descriptive-correlational and comparative design, focusing on bona fide students from Silliman University’s College of Computer Studies during the second semester of 2023-2024. Participants were recruited through a survey sent via school email, with responses collected until March 30, 2024. Data was gathered using a password-protected device and Google Forms, ensuring restricted access to raw data. The demographic profile was summarized, and the prevalence of wrist pain and device usage were analyzed using percentages and weighted means. Statistical analyses included Spearman’s rank correlation coefficient to assess the relationship between wrist pain and device usage and an Independent T-test to evaluate differences in academic performance based on wrist pain presence. Alpha was set at 0.05. Results: The study revealed that 40% of College of Computer Studies students experience wrist pain, with 2 out of every 5 students affected. Laptops and desktops were the most frequently used devices for academic work, achieving a weighted mean of 4.511, while mobile phones and tablets received lower means of 4.183 and 1.911, respectively. The average academic performance score among students was 29.7, classified as ‘Good Performance.’ Notably, there was no significant relationship between the frequency of device usage and wrist pain, as indicated by p-values exceeding 0.05. However, a significant difference in perceived academic performance was observed, with students without wrist pain scoring an average of 30.39 compared to 28.72 for those with wrist pain and a p-value of 0.0134 confirming this distinction. Conclusion: The study revealed that about 40% of students in the College of Computer Studies experience wrist pain, but there is no significant link between device usage and pain occurrence. However, students without wrist pain demonstrated better academic performance than those with pain, suggesting that wrist health may impact academic success. These findings imply that physical therapy practices in the Philippines should focus on preventive strategies and ergonomic education to improve student health and performance.

Keywords: wrist pain, frequency of use of technological devices, perceived academic performance, physical therapy

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Authors: Crispin Rakibu Mbamba

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Twenty-two years after the adoption of the United Nation Trafficking Protocol, evidence suggest that child trafficking continues to rise. Community level factors, like poverty which creates the conditions for children’s vulnerability is key to the rise in trafficking cases in Ghana. Albeit, growing evidence suggestthat despite the vulnerabilities, communities have the capacity to prevent and address child trafficking issues. This study contributes to this positive agenda by exploring the ways in which communities (and the key actors) in Ghana contribute to child trafficking interventions.The study objective is explored through in-depth interviews with practitioners (including social workers) from an organization working in trafficking hotspots in Ghana. Interviews wereanalyzed thematically with the help of HyperRESEARCH software. From the in-depth interviews, three themes were identified as the ways in which communities are involved in child trafficking interventions: 1) engagement of community leaders, 2) community-led anti-trafficking committees and 3) knowledge about trafficking. Albeit the cultural differences, evidence on the instrumental role of community chiefs and leaders provide important learning on how to harness trafficking intervention measures and ensure better child protection practices. Based on the findings, we recommend the need to intensify trafficking awareness campaigns in rural communities where education is lacking to contribute to United Nations (UN) promoting Just, Peaceful and Inclusive societies’ mandate.

Keywords: child trafficking, community interventions, knowledge on trafficking, human trafficking intervention

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Authors: Shohei Maruyama, Yasuo Matsuyama, Sachiyo Aburatani

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The development of the method to annotate unknown gene functions is an important task in bioinformatics. One of the approaches for the annotation is The identification of the metabolic pathway that genes are involved in. Gene expression data have been utilized for the identification, since gene expression data reflect various intracellular phenomena. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway.

Keywords: metabolic pathways, gene expression data, microarray, Kullback–Leibler divergence, KL divergence, support vector machines, SVM, machine learning

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Sai Sankalp Vemavarapu

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This paper discusses the application of machine learning in the field of transportation engineering for predicting engineering properties of pavement more accurately and efficiently. Predicting the elastic properties aid us in assessing the current road conditions and taking appropriate measures to avoid any inconvenience to commuters. This improves the longevity and sustainability of the pavement layer while reducing its overall life-cycle cost. As an example, we have implemented differential evolution (DE) in the back-calculation of the elastic modulus of multi-layered pavement. The proposed DE global optimization back-calculation approach is integrated with a forward response model. This approach treats back-calculation as a global optimization problem where the cost function to be minimized is defined as the root mean square error in measured and computed deflections. The optimal solution which is elastic modulus, in this case, is searched for in the solution space by the DE algorithm. The best DE parameter combinations and the most optimum value is predicted so that the results are reproducible whenever the need arises. The algorithm’s performance in varied scenarios was analyzed by changing the input parameters. The prediction was well within the permissible error, establishing the supremacy of DE.

Keywords: cost function, differential evolution, falling weight deflectometer, genetic algorithm, global optimization, metaheuristic algorithm, multilayered pavement, pavement condition assessment, pavement layer moduli back calculation

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Lexi Li

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This study aims to contribute to the field of how computer technology can be exploited to enhance EFL textbook design. Specifically, the study demonstrates how computerized native and learner corpora can be used to enhance modal verb treatment in EFL textbooks. The linguistic focus is will, would, can, could, may, might, shall, should, must. The native corpus is the spoken component of BNC2014 (hereafter BNCS2014). The spoken part is chosen because the pedagogical purpose of the textbooks is communication-oriented. Using the standard query option of CQPweb, 5% of each of the nine modals was sampled from BNCS2014. The learner corpus is the POS-tagged Ten-thousand English Compositions of Chinese Learners (TECCL). All the essays under the “secondary school” section were selected. A series of five secondary coursebooks comprise the textbook corpus. All the data in both the learner and the textbook corpora are retrieved through the concordance functions of WordSmith Tools (version, 5.0). Data analysis was divided into two parts. The first part compared the patterns of modal verbs in the textbook corpus and BNC2014 with respect to distributional features, semantic functions, and co-occurring constructions to examine whether the textbooks reflect the authentic use of English. Secondly, the learner corpus was compared with the textbook corpus in terms of the use (distributional features, semantic functions, and co-occurring constructions) in order to examine the degree of influence of the textbook on learners’ use of modal verbs. Moreover, the learner corpus was analyzed for the misuse (syntactic errors, e.g., she can sings*.) of the nine modal verbs to uncover potential difficulties that confront learners. The results indicate discrepancies between the textbook presentation of modal verbs and authentic modal use in natural discourse in terms of distributions of frequencies, semantic functions, and co-occurring structures. Furthermore, there are consistent patterns of use between the learner corpus and the textbook corpus with respect to the three above-mentioned aspects, except could, will and must, partially confirming the correlation between the frequency effects and L2 grammar acquisition. Further analysis reveals that the exceptions are caused by both positive and negative L1 transfer, indicating that the frequency effects can be intercepted by L1 interference. Besides, error analysis revealed that could, would, should and must are the most difficult for Chinese learners due to both inter-linguistic and intra-linguistic interference. The discrepancies between the textbook corpus and the native corpus point to a need to adjust the presentation of modal verbs in the textbooks in terms of frequencies, different meanings, and verb-phrase structures. Along with the adjustment of modal verb treatment based on authentic use, it is important for textbook writers to take into consideration the L1 interference as well as learners’ difficulties in their use of modal verbs. The present study is a methodological showcase of the combination both native and learner corpora in the enhancement of EFL textbook language authenticity and appropriateness for learners.

Keywords: EFL textbooks, learner corpus, modal verbs, native corpus

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Authors: Poteet Frances, Glovinski Ira

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INTRODUCTION: The interoceptive nervous system continuously senses chemical and anatomical changes and helps you recognize, understand, and feel what’s going on inside your body so it is important for energy regulation, memory, affect, and sense of self. A newborn needs predictable routines rather than confusion/chaos to make connections between internal experiences and emotions. AIM: Current legal protocols of removing babies from drug-addicted mothers impact the critical window of bonding. The newborn’s brain is social and the attachment process influences a child’s development which begins immediately after birth through nourishment, comfort, and protection. DESCRIPTION: Our project aims to educate drug-addicted mothers, and medical, nursing, and social work professionals on interoceptive concepts and practices to sustain the mother/newborn relationship. A mother’s interoceptive knowledge predicts children’s emotion regulation and social skills in middle childhood. CONCLUSION: When mothers develop an awareness of their inner bodily sensations, they can self-regulate and be emotionally available to co-regulate (support their newborn during distressing emotions and sensations). Our project has enhanced relationship preservation (mothers understand how their presence matters) and the overall mother/newborn connection.

Keywords: drug-addiction, interoception, legal, mothers, newborn, self-regulation

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Authors: LaShawnda N. Fields, Valandra

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As part of a larger study, this particular line of inquiry focuses on experiences of tokenism and invisible labor expected of Black women within social work education. Black women faculty members and doctoral students participated in semi-formal, in-depth interviews. All participants were identified as members of schools of social work within Carnegie-designated R-1 institutions. Several participants believed that their race independently and the intersection of their race and gender was often misrepresented by their institution as an indication of a diverse and equitable environment. These women believed they were often solicited to participate in visual materials and make public appearances to benefit the school while feeling invisible. Most of the Black women interviewed, whether faculty members or doctoral students, were the sole Black person or one of very few Black women at these schools of social work. Similarly, the Black doctoral students spoke of being “paraded around” as a prized show horse while enduring a toxic culture that lacks inclusion. These women expressed frustration and disappointment as their images and scholarship were featured on websites and within marketing materials, not the pride and joy such exposure should elicit. These experiences of tokenism were taking place while the women constantly received messages of not being good enough or not a good fit at their institution. Invisible labor refers to work that is not compensated nor formally recognized. This labor is primarily committee work and student support. Representation of Black women faculty members is limited at these research-intensive schools of social work resulting in these women being sought out by students across disciplines. Similarly, the Black women doctoral students are informally recruited as peer mentors to support those students rising in the ranks behind them. Though this work is rooted in retention efforts, it is never identified as such. All participants identified committee work related to their identities as another way they find themselves engaged in work that often goes unrecognized and underappreciated. Committee work is usually tied to identity work, such as diversity, equity, and inclusion though it rarely translates to action and improvements. This qualitative study provides insight into the lived experiences of an at-risk and under-represented demographic. Institutions can better understand how they can support this demographic. These Black women scholars have been invited into these institutions but have not historically been granted full access. These women have survived unsavory conditions through sheer determination and support found mostly outside their schools of social work. Utilizing this data as a springboard for informed and action-oriented strategic planning would allow institutions to create inclusive and equity cultures that result in Black women thriving versus simply surviving.

Keywords: education, equity, invisible labor, tokenism, intersectionality

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