Search results for: adult learning

Authors: Yeimis Milton, Palomino Pichihua

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The city, like living organisms, originates from codes, structured information in the form of rules that condition the physical form and performance of urban space. Usually, the so-called urban codes clash with the spontaneous nature of the city, with the urban Kháos that contextualizes the free creation (poiesis) of human collectives. This contradiction is especially evident in Latin America, which, like other developing regions, lacks adequate instruments to guide urban growth. Thus constructing a hybrid between the formal and informal city, categories that are difficult to separate one from the other. This is a comparative study focusing on the urban codes created to address the pandemic. The objective is to build an overview of these innovations in the region. The sample is made up of official norms published in pandemic, directly linked to urban planning and building control (urban form). The countries analyzed are Brazil, Mexico, Argentina, Peru, Colombia, and Chile. The study uncovers a shared interest in facing future urban problems, in contrast to the inconsistency of proposed legal instruments. Factors such as the lack of articulation, validity time, and ambiguity, among others, accentuate this problem. Likewise, it evidences that the political situation of each country has a significant influence on the development of these norms and the possibility of their long-term impact. In summary, the global emergency has produced opportunities to transform urban systems from their internal rules; however, there are very few successful examples in this field. Therefore, Latin American cities have the task of learning from this defeat in order to lay the foundations for a more resilient and sustainable urban future.

Keywords: pandemic, regulation, urban planning, latin America

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Taekyong Lee, Joon-Young Kim, Jae-Min Cha

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Piping and instrumentation diagram (P&ID) is an essential design drawing describing the interconnection of process equipment and the instrumentation installed to control the process. P&IDs are modified and managed throughout a whole life cycle of a process plant. For the ease of data transfer, P&IDs are generally handed over from a design company to an engineering company as portable document format (PDF) which is hard to be modified. Therefore, engineering companies have to deploy a great deal of time and human resources only for manually converting P&ID images into a computer aided design (CAD) file format. To reduce the inefficiency of the P&ID conversion, various symbols and texts in P&ID images should be automatically recognized. However, recognizing information in P&ID images is not an easy task. A P&ID image usually contains hundreds of symbol and text objects. Most objects are pretty small compared to the size of a whole image and are densely packed together. Traditional recognition methods based on geometrical features are not capable enough to recognize every elements of a P&ID image. To overcome these difficulties, state-of-the-art deep learning models, RetinaNet and connectionist text proposal network (CTPN) were used to build a system for recognizing symbols and texts in a P&ID image. Using the RetinaNet and the CTPN model carefully modified and tuned for P&ID image dataset, the developed system recognizes texts, equipment symbols, piping symbols and instrumentation symbols from an input P&ID image and save the recognition results as the pre-defined extensible markup language format. In the test using a commercial P&ID image, the P&ID information recognition system correctly recognized 97% of the symbols and 81.4% of the texts.

Keywords: object recognition system, P&ID, symbol recognition, text recognition

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Authors: Chanada Aonsri, Wichai Eungpinichpong

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Stereotypical behavior is one of the learning and social skills development problems that affect children with autism. Previous studies found that traditional Thai massage (TTM) could reduce stereotypical behaviors in autistic children. However, the effects of TTM delivered by the parents of autistic children have not been explored. This pilot study investigated the effects of TTM by parents on stereotypical behaviors in children with autism. A one-group pretest-posttest design was applied for 15 children, aged 4-16 years, with their parents' permissions. They participated in the study at the Special Education program of the Special Education Center of Khon Kaen University, Thailand. After being trained in a specialized TTM for children, the parents delivered 50-minute TTM to children once a day, twice a week for eight weeks. The severity of autism and autistic behaviors were measured using the Childhood Autism Rating Scale (CARS), and the Autism Treatment Evaluation Checklist (ATEC), respectively. The functions of autonomic nervous systems were measured using Heart Rate Variability (HRV) to indicated physical and mental disorders such as stress. The data at baseline and the 8th week were analyzed using either an independent t-test or Wilcoxon signed-rank test. The study found that 16 sessions of TTM significantly improved measured data for autism in all children including the CARS (p<0.001), ATEC, speech/language/communication (p<0.001), sociability (p<0.001), sensory/cognitive awareness (p<0.001), health/physical/behavior (p < 0.001), and HRV (p<0.001). The results indicated that TTM performed by parents could be useful as an adjunct therapy for autistic children as it can reduce stereotypical behaviors and stress.

Keywords: traditional Thai massage, stereotypical behaviors, Autistic children, parent

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Authors: Akano Benedict Ubawuike

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This study investigated the effects of pre-laboratory discussion on physics students’ acquisition of science process skills. The study design was quasi-experimental and purposive sampling technique was applied in selecting two schools in Kontagora Town for the research based on the availability of a good physics laboratory. Intact classes already grouped by the school for the sake of small laboratory space and equipment, comprising Thirty (30) students, 15 for experimental group in School A and 15 for control in school B were the subjects for the research. The instrument used for data collection was the lesson prepared for pre – practical discussion and researcher made Science Process Skill Test (SPST ) and two (2) research questions, and two (2) research hypotheses were developed to guide the study. The data collected were analyzed using means and t-Test statistics at 0.05 level of significance. The study revealed that pre-laboratory discussion was found to be more efficacious in enhancing students’ acquisition of science process skills. It also revealed that gender, had no significant effect on students’ acquisition of science process skills. Based on the findings, it was recommended among others that teachers should encourage students to develop interest in practical activities by engaging them in pre-laboratory discussion and providing instructional materials that will challenge them to be actively involved during practical lessons. It is also recommended that Ministries of Education and professional organizations like Science Teachers' Association of Nigeria (STAN) should organize workshops, seminars and conferences for physics teachers and Physics concepts should be taught with practical activity so that the students will do science instead of learning about science.

Keywords: physics, laboratory, discussion, students, acquisition, science process skills

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Authors: Maaly Jarrah

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Oral fluency in the target language is what many second language learners hope to achieve by living abroad. Research in the past has demonstrated the role informal environments play in improving L2 learners' oral fluency. However, living in the target country and being part of its community does not ensure the development of oral fluency skills. L2 learners' desire to communicate and access to speaking opportunities in the host community are key in achieving oral fluency in the target language. This study attempts to identify differences in oral fluency, specifically speech rate, between learners who communicate in the L2 outside the classroom and those who do not. In addition, as the desire to communicate is a crucial factor in developing oral fluency, this study investigates whether or not learners' desire to speak the L2 outside the classroom plays a role in their frequency of L2 use outside the classroom. Finally, given the importance of the availability of speaking opportunities for L2 learners in order to practice their speaking skills, this study reports on the participants' perceptions of the speaking opportunities accessible to them in the target community while probing whether or not their perceptions differed based on their oral fluency level and their desire to communicate. The results suggest that exposure to the target language and daily communication with the native speakers is strongly related to the development of learners' oral fluency. Moreover, the findings suggest that learners' desire to communicate affects their frequency of communication in their L2 outside the classroom. At the same time, all participants, regardless of their oral fluency level and their desire to communicate, asserted that speaking opportunities beyond the classroom are very limited. Finally, the study finds there are marked differences in the perceptions learners have regarding opportunities for learning offered by the same language program. After reporting these results, the study concludes with recommendations for ESL programs that serve international students.

Keywords: ESL programs, L2 Learners, oral fluency, second language

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Authors: Yi-Hsiu Lai

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The present study aimed to elucidate the lexical-semantic processing for Chinese as second language (CSL) learners. Twenty L1 speakers of Chinese and twenty CSL learners in Taiwan participated in a picture naming task and a category fluency task. Based on their Chinese proficiency levels, these CSL learners were further divided into two sub-groups: ten CSL learners of elementary Chinese proficiency level and ten CSL learners of intermediate Chinese proficiency level. Instruments for the naming task were sixty black-and-white pictures: thirty-five object pictures and twenty-five action pictures. Object pictures were divided into two categories: living objects and non-living objects. Action pictures were composed of two categories: action verbs and process verbs. As in the naming task, the category fluency task consisted of two semantic categories – objects (i.e., living and non-living objects) and actions (i.e., action and process verbs). Participants were asked to report as many items within a category as possible in one minute. Oral productions were tape-recorded and transcribed for further analysis. Both error types and error frequency were calculated. Statistical analysis was further conducted to examine these error types and frequency made by CSL learners. Additionally, category effects, pictorial effects and L2 proficiency were discussed. Findings in the present study helped characterize the lexical-semantic process of Chinese naming in CSL learners of different Chinese proficiency levels and made contributions to Chinese vocabulary teaching and learning in the future.

Keywords: lexical-semantic processing, Mandarin Chinese, naming, category effects

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Authors: Jiangxia Han, Liang Xue, Keda Chen

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Despite the significant progress over the last decades in reservoir simulation using numerical discretization, meshing is complex. Moreover, the high degree of freedom of the space-time flow field makes the solution process very time-consuming. Therefore, we present Physics-Informed Convolutional Neural Networks(PICNN) as a hybrid scientific theory and data method for reservoir modeling. Besides labeled data, the model is driven by the scientific theories of the underlying problem, such as governing equations, boundary conditions, and initial conditions. PICNN integrates governing equations and boundary conditions into the network architecture in the form of a customized convolution kernel. The loss function is composed of data matching, initial conditions, and other measurable prior knowledge. By customizing the convolution kernel and minimizing the loss function, the neural network parameters not only fit the data but also honor the governing equation. The PICNN provides a methodology to model and history-match flow and transport problems in porous media. Numerical results demonstrate that the proposed PICNN can provide an accurate physical solution from a limited dataset. We show how this method can be applied in the context of a forward simulation for continuous problems. Furthermore, several complex scenarios are tested, including the existence of data noise, different work schedules, and different good patterns.

Keywords: convolutional neural networks, deep learning, flow and transport in porous media, physics-informed neural networks, reservoir simulation

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Authors: Gysele Lima Ricci, Jose Miguel Rodriguez Anton

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Companies in the tourism sector need to achieve competitive advantages for their survival in the market. In this way, the models based on association, cooperation, complementarity, distribution, exchange and mutual assistance arise as a possibility of organizational development, taking as reference the concept of networks. Many companies seek to partner in local networks as clusters to act together and associate. The main objective of the present research is to identify the specificities of management and the practices of cooperation in the tourist destination of São Paulo - Brazil, and to propose a new management model with possible cluster of tourism. The empirical analysis was carried out in three phases. As a first phase, a research was made by the companies, associations and tourism organizations existing in São Paulo, analyzing the characteristics of their business. In the second phase, the management specificities and cooperation practice used in the tourist destination. And in the third phase, identifying the possible strengths and weaknesses that potential or potential tourist cluster could have, proposing the development of the management model of the same adapted to the needs of the companies, associations and organizations. As a main result, it has been identified that companies, associations and organizations could be looking for synergies with each other and collaborate through a Hiperred organizational structure, in which they share their knowledge, try to make the most of the collaboration and to benefit from three concepts: flexibility, learning and collaboration. Finally, it is concluded that, the proposed tourism cluster management model is viable for the development of tourism destinations because it makes it possible to strategically address agents which are responsible for public policies, as well as public and private companies and organizations in their strategies competitiveness and cooperation.

Keywords: cluster, management model, networks, tourism sector

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Authors: Siripen Yiamjanya, Kevin Wongleedee

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This research paper aims to identify travel motivation by push and pull factors that affected decision making of international tourists in selecting Thailand as their destination choice. A total of 200 international tourists who traveled to Thailand during January and February, 2014 were used as the sample in this study. A questionnaire was employed as a tool in collecting the data, conducted in Bangkok. The list consisted of 30 attributes representing both psychological factors as “push- based factors” and destination factors as “pull-based factors”. Mean and standard deviation were used in order to find the top ten travel motives that were important determinants in the respondents’ decision making process to select Thailand as their destination choice. The finding revealed the top ten travel motivations influencing international tourists to select Thailand as their destination choice included [i] getting experience in foreign land; [ii] Thai food; [iii] learning new culture; [iv] relaxing in foreign land; [v] wanting to learn new things; [vi] being interested in Thai culture, and traditional markets; [vii] escaping from same daily life; [viii] enjoying activities; [ix] adventure; and [x] good weather. Classification of push- based and pull- based motives suggested that getting experience in foreign land was the most important push motive for international tourists to travel, while Thai food portrayed its highest significance as pull motive. Discussion and suggestions were also made for tourism industry of Thailand.

Keywords: decision making, destination choice, international tourist, pull factor, push factor, Thailand, travel motivation

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Authors: Makrina Tindangen

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The problems faced by participants of teacher profession program in Faculty of teaching and education Mulawarman University is professional and pedagogic competence. Professional competence related to the mastery of teaching materials, while pedagogic competence related with the ability to plan and to implement learning. Based on the problems, the purpose of the research is to maximize the role of companion teacher for the achievement of professional and pedagogic competencies in the workshop of the participants of teacher professional education in the Faculty of Teaching and Education of Mulawarman University. Qualitative research method with interview guidance and document to get in-depth data on how to maximize the role of companion teachers in the achievement of professional and pedagogic competencies in the workshop participants of professional education participants. Location of this research is on the Faculty of Teaching and Education of Mulawarman University, Samarinda City, East Kalimantan Province. Research respondents were 12 teachers of workshop facilitator. Descriptive data analysis is through interpretation of interview data. The conclusion of the research result, how to maximize the role of assistant teachers in workshop activities for the professional competence and pedagogic competence of professional teacher training program participants, through facilitation activities conducted by teachers of companion related to real problems faced by students in school, so that the workshop participants have professional competence and pedagogic as an initial competence before carrying out practical activities of field experience in school.

Keywords: companion teacher, professional and pedagogical competence, activities, workshop participants

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Authors: Jude K. Safo

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Knowledge Graphs KG) and their relation to Graph Embeddings (GE) represent a unique data structure in the landscape of machine learning (relative to image, text and acoustic data). Unlike the latter, GEs are the only data structure sufficient for representing hierarchically dense, semantic information needed for use-cases like supply chain data and protein folding where the search space exceeds the limits traditional search methods (e.g. page-rank, Dijkstra, etc.). While GEs are effective for compressing low rank tensor data, at scale, they begin to introduce a new problem of ’data retreival’ which we observe in Large Language Models. Notable attempts by transE, TransR and other prominent industry standards have shown a peak performance just north of 57% on WN18 and FB15K benchmarks, insufficient practical industry applications. They’re also limited, in scope, to next node/link predictions. Traditional linear methods like Tucker, CP, PARAFAC and CANDECOMP quickly hit memory limits on tensors exceeding 6.4 million nodes. This paper outlines a topological framework for linear mapping between concepts in KG space and GE space that preserve cardinality. Most importantly we introduce a traceable framework for composing dense linguistic strcutures. We demonstrate performance on WN18 benchmark this model hits. This model does not rely on Large Langauge Models (LLM) though the applications are certainy relevant here as well.

Keywords: representation theory, large language models, graph embeddings, applied algebraic topology, applied knot theory, combinatorics

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Authors: D. Shamatov

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Becoming a teacher is a journey with significant learning experiences. Exploring teachers’ lives and experiences can provide much-needed insights into the multiple realities of teaching. Teachers’ stories through qualitative narrative studies help understand and appreciate the complexities of the socio-political, economic and practical realities facing teachers. Events and experiences, both past and present, that take place at home, school, and in the broader social sphere help to shape these teachers’ lives and careers. Researchers and educators share the responsibility of listening to these teachers’ stories and life experiences and being sensitive to their voices in order to develop effective models for teacher development. A better understanding of how teachers learn to become teachers can help teacher educators prepare more effective teacher education programs. This paper is based on qualitative research which includes individual and focus group interviews, as well as auto-biography stories of Master of Science in School Leadership students at Graduate School of Education of Nazarbayev University. Twenty five MSc students from across Kazakhstan reflected on their professional journey and wrote their professional autobiographies as teachers. Their autobiographies capture the richness of their experiences and beliefs as a teacher, but also serve as window to understand broader socio-economic and political contexts where these teachers live and work. The study also provides an understanding of the systemic and socio-economic challenges of teachers in the context of post-Soviet Kazakhstan. It helps the reader better understand how wider societal forces interact and frame the development of teachers. The paper presents the findings from these stories of MSc students and offers some practical and policy implications for teacher preparation and teacher development.

Keywords: becoming a teacher, Kazakhstan, teacher stories, teacher development

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Authors: Robert Ogbanje Okwori

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The paper explained the causes low students’ access to university education in Nigeria and how it can be remedied. It is discovered that low students’ access to university education in Nigeria is evident despite these number of universities in the country. In 2006/2007 academic session, 806,089 sat for Joint Unified Matriculation Board Examination (JAMB) into Nigerian universities and only 123,626 (15.3%) were admitted while 2011/2012 academic session, a total of 1,493,604 candidates sat for Joint Unified Matriculation Board Examination (JAMB) into Nigerian universities and only 65,073 (43.57%) were admitted. This necessitates for the research. Therefore, the study posed the following research questions. What are causes of low students’ access to university education in Nigeria? What are the challenges of students’ access to university education in Nigeria? How can students’ access to university education in Nigeria be improved? Sample survey research design was adopted for the study. A structured questionnaire was used to gather data for the study. Six hundred and eighty (680) respondents which comprised of 100 level university students; JAMB Officers and University administrators (Vice Chancellors, Registrars and Admission Officers) were used for the study. Stratified random sampling was applied for adequate representation of respondents from universities in the six geopolitical zones of Nigeria. Mean was used to answer research questions while Kuder-Richardson formula 20 was used to check the internal consistency of the instrument. The correlation coefficient of the instrument was 0.87. The major findings include the carrying capacity of each university contributes to low students’ access to university education and academic staff were inadequate. From the analysis of the study, it is concluded that the rate of access to university education is low, therefore, every university should establish distance learning programme to reduce university admission crisis. The training infrastructure in the universities should be improved upon by the owners to increase the carrying capacity of each university.

Keywords: access, causes, low, university

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Authors: Kalangi Rodrigo

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The term Neuroecology elucidates the study of customizable variation in cognition and the brain. Subject marked the birth since 1980s, when researches began to apply methods of comparative evolutionary biology to cognitive processes and the underlying neural mechanisms of cognition. In Archaeology and Anthropology, we observe behaviors such as social learning skills, innovative feeding and foraging, tool use and social manipulation to determine the cognitive processes of ancient mankind. Depending on the brainstem size was used as a control variable, and phylogeny was controlled using independent contrasts. Both disciplines need to enriched with comparative literature and neurological experimental, behavioral studies among tribal peoples as well as primate groups which will lead the research to a potential end. Neuroecology examines the relations between ecological selection pressure and mankind or sex differences in cognition and the brain. The goal of neuroecology is to understand how natural law acts on perception and its neural apparatus. Furthermore, neuroecology will eventually lead both principal disciplines to Ethology, where human behaviors and social management studies from a biological perspective. It can be either ethnoarchaeological or prehistoric. Archaeology should adopt general approach of neuroecology, phylogenetic comparative methods can be used in the field, and new findings on the cognitive mechanisms and brain structures involved mating systems, social organization, communication and foraging. The contribution of neuroecology to archaeology and anthropology is the information it provides on the selective pressures that have influenced the evolution of cognition and brain structure of the mankind. It will shed a new light to the path of evolutionary studies including behavioral ecology, primate archaeology and cognitive archaeology.

Keywords: Neuroecology, Archaeology, Brain Evolution, Cognitive Archaeology

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Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: Nimrod Bantigue

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The Department of Education is continuously providing safe teaching-learning facilities and hazard-free environments to the learners. To achieve this goal, teachers’ awareness of DepEd’s DRRM programs and activities is extremely important; thus, this descriptive correlational quantitative study was conceptualized. This research answered four questions on the profile and level of awareness of the 153 teacher respondents of Oriental Mindoro National High School for the academic year 2018-2019. Stratified proportional sampling was employed, and both descriptive and inferential statistics were utilized to treat data. The findings revealed that the majority of the teachers at OMNHS are female and are in the age bracket of 20-40. Most are married and pursue graduate studies. They have moderate awareness of the Department of Education’s DRRM programs and activities in terms of assessment of risks activities, planning activities, implementation activities during disaster and evaluation and monitoring activities with 3.32, 3.12, 3.40 and 3.31 as computed means, respectively. Further, the result showed a significant relationship between the profile of the respondents such as age, civil status and educational attainment and the level of awareness. On the contrary, sex does not have a significant relationship with the level of awareness. The Support School DRRM program with Utilization Guide on School DRRM Manual was proposed to increase, improve and strengthen the weakest areas of awareness rated in each DRRM activity, such as assessment of risks, planning, and implementation during disasters and monitoring and evaluation.

Keywords: awareness, management, monitoring, risk reduction

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Martina Dickson, Lilly Tennant

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The young Emirati female university students of today are the first generation of women in the UAE for whom higher education as become not only a possibility, but almost an expectation. Young women in the UAE today make up around 77% of students in higher education institutes in the country. However, the societal expectations placed upon these women in terms of early marriage, child-bearing and rearing are similar to those placed upon their mothers and grandmothers in a time where women were not expected to go to university. A large proportion of female university students in the UAE are mothers of young children, or become mothers whilst at the university. This creates a challenging situation for young student mothers, where two weeks’ maternity leave is typical across institutions. The context of this study is in one such institution in the UAE. We have employed a mixed method approach to gathering interview data from twenty mothers, and survey data from over one hundred mothers. The main findings indicate that mothers have strong desires for their institution to support them more, for example by the provision of nursery facilities and resting areas for new mothers, and giving them greater flexibility over course selections and schedules including the provision of online learning. However, the majority felt supported on a personal level by their tutors. The major challenges which they identified in returning to college after only two weeks’ leave included the inevitable health and lack of sleep issues when caring for a newborn, struggling to catch up with missed college work and handling their course load. We also explored the women's’ home support systems which were provided from a variety of extended family, spouses and paid domestic help.

Keywords: student mothers, challenges, supports, United Arab Emirates

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Authors: Broto Seno

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This study aims to describe and examine more in the implementation of educational partnerships for undergraduate students at Yogyakarta State University (YSU), which is more focused on educational partnerships abroad. This study used descriptive qualitative approach. The study subjects consisted of a vice-rector, two staff education partnerships, four vice-dean, nine undergraduate students and three foreign students. Techniques of data collection using interviews and document review. Validity test of the data source using triangulation. Data analysis using flow models Miles and Huberman, namely data reduction, data display, and conclusion. Results of this study showed that the implementation of educational partnerships abroad for undergraduate students at YSU meets six of the nine indicators of the success of strategic partnerships. Six indicators are long-term, strategic, mutual trust, sustainable competitive advantages, mutual benefit for all the partners, and the separate and positive impact. The indicator has not been achieved is cooperative development, successful, and world class / best practice. These results were obtained based on the discussion of the four formulation of the problem, namely: 1) Implementation and development of educational partnerships abroad has been running good enough, but not maximized. 2) Benefits of the implementation of educational partnerships abroad is providing learning experiences for students, institutions of experience in comparison to each faculty, and improving the network of educational partnerships for YSU toward World Class University. 3) The sustainability of educational partnerships abroad is pursuing a strategy of development through improved management of the partnership. 4) Supporting factors of educational partnerships abroad is the support of YSU, YSU’s partner and society. Inhibiting factors of educational partnerships abroad is not running optimally management.

Keywords: partnership, education, YSU, institutions and faculties

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Authors: Vesile Unver, Tulay Basak, Hatice Ayhan, Ilknur Cinar, Emine Iyigun, Nuran Tosun

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The aim of this study is to analyze the effects of the hybrid simulation. In this simulation, types standardized patients and task trainers are employed simultaneously. For instance, in order to teach the IV activities standardized patients and IV arm models are used. The study was designed as a quasi-experimental research. Before the implementation an ethical permission was taken from the local ethical commission and administrative permission was granted from the nursing school. The universe of the study included second-grade nursing students (n=77). The participants were selected through simple random sample technique and total of 39 nursing students were included. The views of the participants were collected through a feedback form with 12 items. The form was developed by the authors and “Patient intervention self-confidence/competence scale”. Participants reported advantages of the hybrid simulation practice. Such advantages include the following: developing connections between the simulated scenario and real life situations in clinical conditions; recognition of the need for learning more about clinical practice. They all stated that the implementation was very useful for them. They also added three major gains; improvement of critical thinking skills (94.7%) and the skill of making decisions (97.3%); and feeling as if a nurse (92.1%). In regard to the mean scores of the participants in the patient intervention self-confidence/competence scale, it was found that the total mean score for the scale was 75.23±7.76. The findings obtained in the study suggest that the hybrid simulation has positive effects on the integration of theoretical and practical activities before clinical activities for the nursing students.

Keywords: hybrid simulation, clinical practice, nursing education, nursing students

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Authors: Tom Wanyama

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We have developed a weight-based parts inventory monitoring system utilizing the Industrial Internet of Things (IIoT) to enhance operational efficiencies in manufacturing. The system addresses various challenges, including eliminating downtimes caused by stock-outs, preventing human errors in parts delivery and product assembly, and minimizing motion waste by reducing unnecessary worker movements. The system incorporates custom QR codes for simplified inventory tracking and retrieval processes. The generated data serves a dual purpose by enabling real-time optimization of parts flow within manufacturing facilities and facilitating retroactive optimization of stock levels for informed decision-making in inventory management. The pilot implementation at SEPT Learning Factory successfully eradicated data entry errors, optimized parts delivery, and minimized workstation downtimes, resulting in a remarkable increase of over 10% in overall equipment efficiency across all workstations. Leveraging the IIoT features, the system seamlessly integrates information into the process control system, contributing to the enhancement of product quality. This approach underscores the importance of effective tracking of parts inventory in manufacturing to achieve transparency, improved inventory control, and overall profitability. In the broader context, our inventory monitoring system aligns with the evolving focus on optimizing supply chains and maintaining well-managed warehouses to ensure maximum efficiency in the manufacturing industry.

Keywords: industrial Internet of things, industrial systems integration, inventory monitoring, inventory control in manufacturing

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Authors: Abeer Alhazmi

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Introduction: Clinical education is considered a significant part of the learning process for nurses and nursing students. However, recruiting high- caliber individuals to train them to be tomorrow’s educators/teachers has been a recurrent challenge. One of the troubling challenges in this field is the absent of proper training programmes to train educators to be future education professionals and leaders. Aim: To explore the impact of a stage 1 and stage 2 clinical instructor courses on developing leadership skills for nurses as educators.Theoretical Framework: Informed by a symbolic interactionist framework, this research explored the Impact of stage 1 and stage 2 clinical instructor courses on nurses' knowledge, attitudes, and leadership skills. Method: Using Glaserian grounded theory method the data were derived from 3 focus groups and 15 in-depth interviews with nurse educators/clinical instructors and nurses who attended stage 1 and stage 2 clinical instructor courses at King Abdu-Aziz University Hospital (KAUH). Findings: The findings of the research are represented in the core category exploring new identity as educator and its two constituent categories Accepting change, and constructing educator identity. The core and sub- categories were generated through a theoretical exploration of the development of educator’s identity throughout stage 1 and stage 2 clinical instructor courses. Conclusion: The social identity of the nurse educators was developed and changed during and after attending stage 1 and stage 2 clinical instructor courses. In light of an increased understanding of the development process of educators identity and role, the research presents implications and recommendations that may contribute to the development of nursing educators in general and in Saudi Arabia in specific.

Keywords: clinical instructor course, educators, identity work, clinical nursing

Procedia PDF Downloads 416

Authors: Alisher Ibragimov, Sofya Senotrusova, Aleksandra Beliaeva, Egor Ushakov, Yuri Markin

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Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification.

Keywords: breast cancer, computer-aided diagnosis, deep learning, multi-view mammogram, siamese neural network

Procedia PDF Downloads 138

Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

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Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

Procedia PDF Downloads 180

Authors: Prakash Singh

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Many schools throughout the world are facing constant pressure to cope with the violence and drug abuse of learners who show little or no respect for acceptable and desirable social norms. These delinquent learners tend to harbour feelings of being beyond reproach because they strongly believe that it is well within their rights to engage in violent and destructive behaviour. Knives, guns, and other weapons appear to be more readily used by them on the school premises than before. It is known that learners smoke, drink alcohol, and use drugs during school hours, hence, their ability to concentrate, work, and learn, is affected. They become violent and display disruptive behaviour in their classrooms as well as on the school premises, and this atrocious behaviour makes it possible for drug dealers and gangsters to gain access onto the school premises. The primary purpose of this exploratory quantitative study was therefore to establish how tobephobia (TBP), caused by school violence and drug abuse, affects teaching and learning in schools. The findings of this study affirmed that poor discipline resulted in producing poor quality education. Most of the teachers in this study agreed that educating learners who consumed alcohol and other drugs on the school premises resulted in them suffering from TBP. These learners are frequently abusive and disrespectful, and resort to violence to seek attention. As a result, teachers feel extremely demotivated and suffer from high levels of anxiety and stress. The word TBP will surely be regarded as a blessing by many teachers throughout the world because finally, there is a word that will make people sit up and listen to their problems that cause real fear and anxiety in schools.

Keywords: aims and objectives of quality education, debilitating effects of tobephobia, fear of failure associated with education, learners' violent behaviour and drug abuse

Procedia PDF Downloads 278

Authors: Christian Ndubisi Madu, Benjamin C. Ozumba, Ifeanyi E. Madu, Valentine E. Nnadi, Ikenna C. Ezeasor

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The ability to generate power and achieve energy security is one of the driving forces behind the emerging ‘microgrid’ concept. Traditional power supply often operates with centralized infrastructure for generating, transmitting and distributing electricity. The inefficiency and the incessant power outages associated with the centralized power supply system in Nigeria has alienated many users who frequently turn to electric power generator sets to power their homes and offices. Such acts are unsustainable and lead to increase in the use of fossil fuels, generation of carbon dioxide emissions and other gases, and noise pollution. They also pose significant risks as they entail random purchases and storage of gasolines which are fire hazards. It is therefore important that organizations rethink their relationships to centralized power suppliers in other to improve energy accessibility and security. This study explores the energy planning processes and learning taking place at the University of Nigeria Enugu Campus as the school lead microgrid feasibility studies in its community. There is need to develop community partners to deal with the issue of energy efficiency and also to create a strategic alliance to confront political, regulatory and economic barriers to locally-based energy planning. Community-based microgrid can help to reduce the cost of adoption and diversify risks. This study offers insights into the ways in which microgrids can further democratize energy planning, procurement, and access, while simultaneously promoting efficiency and sustainability.

Keywords: microgrid, energy efficiency, sustainability, energy security

Procedia PDF Downloads 374

Authors: Ruvaitha Banu, Amaladhithyan Krishnamoorthy

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IoT networks today solve various consumer problems, from home automation systems to aiding in driving autonomous vehicles with the exploration of multiple devices. For example, in an autonomous vehicle environment, multiple sensors are available on roads to monitor weather and road conditions and interact with each other to aid the vehicle in reaching its destination safely and timely. IoT systems are predominantly dependent on the cloud environment for data storage, and computing needs that result in latency problems. With the advent of Fog networks, some of this storage and computing is pushed to the edge/fog nodes, saving the network bandwidth and reducing the latency proportionally. Managing the data stored in these fog nodes becomes crucial as it might also store sensitive information required for a certain application. Data management in fog nodes is strenuous because Fog networks are dynamic in terms of their availability and hardware capability. It becomes more challenging when the nodes in the network also live a short span, detaching and joining frequently. When an end-user or Fog Node wants to access, read, or write data stored in another Fog Node, then a new protocol becomes necessary to access/manage the data stored in the fog devices as a conventional static way of managing the data doesn’t work in Fog Networks. The proposed solution discusses a protocol that acts by defining sensitivity levels for the data being written and read. Additionally, a distinct data distribution and replication model among the Fog nodes is established to decentralize the access mechanism. In this paper, the proposed model implements stewardship towards the data stored in the Fog node using the application of Reinforcement Learning so that access to the data is determined dynamically based on the requests.

Keywords: IoT, fog networks, data stewardship, dynamic access policy

Procedia PDF Downloads 59

Authors: Moquan Gong

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In recent years, with the rapid development of deep learning, pre-training technology for natural language processing has made great progress. The early field of natural language processing has long used word vector methods such as Word2Vec to encode text. These word vector methods can also be regarded as static pre-training techniques. However, this context-free text representation brings very limited improvement to subsequent natural language processing tasks and cannot solve the problem of word polysemy. ELMo proposes a context-sensitive text representation method that can effectively handle polysemy problems. Since then, pre-training language models such as GPT and BERT have been proposed one after another. Among them, the BERT model has significantly improved its performance on many typical downstream tasks, greatly promoting the technological development in the field of natural language processing, and has since entered the field of natural language processing. The era of dynamic pre-training technology. Since then, a large number of pre-trained language models based on BERT and XLNet have continued to emerge, and pre-training technology has become an indispensable mainstream technology in the field of natural language processing. This article first gives an overview of pre-training technology and its development history, and introduces in detail the classic pre-training technology in the field of natural language processing, including early static pre-training technology and classic dynamic pre-training technology; and then briefly sorts out a series of enlightening technologies. Pre-training technology, including improved models based on BERT and XLNet; on this basis, analyze the problems faced by current pre-training technology research; finally, look forward to the future development trend of pre-training technology.

Keywords: natural language processing, pre-training, language model, word vectors

Procedia PDF Downloads 57