Search results for: incremental learning

Authors: Mehrnoosh Abouzari, Shahrokh Sahraei

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Today, with the increase in quantity and quality and variety of crimes, the discussion of crime prevention has faced a serious challenge that human resources alone and with traditional methods will not be effective. One of the developments in the modern world is the presence of artificial intelligence in various fields, including criminal law. In fact, the use of artificial intelligence in criminal investigations and fighting crime is a necessity in today's world. The use of artificial intelligence is far beyond and even separate from other technologies in the struggle against crime. Second, its application in criminal science is different from the discussion of prevention and it comes to the prediction of crime. Crime prevention in terms of the three factors of the offender, the offender and the victim, following a change in the conditions of the three factors, based on the perception of the criminal being wise, and therefore increasing the cost and risk of crime for him in order to desist from delinquency or to make the victim aware of self-care and possibility of exposing him to danger or making it difficult to commit crimes. While the presence of artificial intelligence in the field of combating crime and social damage and dangers, like an all-seeing eye, regardless of time and place, it sees the future and predicts the occurrence of a possible crime, thus prevent the occurrence of crimes. The purpose of this article is to collect and analyze the studies conducted on the use of artificial intelligence in predicting and preventing crime. How capable is this technology in predicting crime and preventing it? The results have shown that the artificial intelligence technologies in use are capable of predicting and preventing crime and can find patterns in the data set. find large ones in a much more efficient way than humans. In crime prediction and prevention, the term artificial intelligence can be used to refer to the increasing use of technologies that apply algorithms to large sets of data to assist or replace police. The use of artificial intelligence in our debate is in predicting and preventing crime, including predicting the time and place of future criminal activities, effective identification of patterns and accurate prediction of future behavior through data mining, machine learning and deep learning, and data analysis, and also the use of neural networks. Because the knowledge of criminologists can provide insight into risk factors for criminal behavior, among other issues, computer scientists can match this knowledge with the datasets that artificial intelligence uses to inform them.

Keywords: artificial intelligence, criminology, crime, prevention, prediction

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Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

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Authors: Rosa Tiziana Bruno

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The connection between education and sustainability has been posited to have benefit for realizing a social development compatible with environmental protection. However, an educational paradigm based on the passage of information or on the fear of a catastrophe might not favor the acquisition of eco-identity. To build a sustainable world, it is necessary to "become people" in harmony with other human beings, being aware of belonging to the same human community that is part of the natural world. This can only be achieved within an authentic educating community and the most effective tools for building educating communities are reading literacy and storytelling. This paper is the report of a research-action carried out in this direction, in agreement with the sociology department of the University of Salerno, which involved four hundred children and their teachers in a path based on the combination of reading literacy, storytelling, autobiographical writing and outdoor education. The goal of the research was to create an authentic educational community within the school, capable to encourage the acquisition of an eco-identity by the pupils, that is, personal and relational growth in the full realization of the Self, in harmony with the social and natural environment, with a view to an authentic education for sustainability. To ensure reasonable validity and reliability of findings, the inquiry started with participant observation and a process of triangulation has been used including: semi-structured interview, socio-semiotic analysis of the conversation and time budget. Basically, a multiple independent sources of data was used to answer the questions. Observing the phenomenon through multiple "windows" helped to comparing data through a variety of lenses. All teachers had the experience of implementing a socio-didactic strategy called "Fiabadiario" and they had the possibility to use it with approaches that fit their students. The data being collected come from the very students and teachers who are engaged with this strategy. The educational path tested during the research has produced sustainable relationships and conflict resolution within the school system and between school and families, creating an authentic and sustainable learning community.

Keywords: educating community, education for sustainability, literature in education, social relations

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Authors: Mohaya Farzin, Parvin Babaei, Mohammad Rostampour

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Ghrelin plays a considerable role in important neurological effects related to food intake and energy homeostasis. As was found, regular physical activity may make available significant improvements to cognitive functions in various behavioral situations. Anxiety is one of the main concerns of the modern world, affecting millions of individuals’ health. There are contradictory results regarding ghrelin's effects on anxiety-like behavior, and the plasma level of this peptide is increased during physical activity. Here we aimed to evaluate the coincident effects of exogenous ghrelin and aerobic exercise on anxiety-like behavior and passive avoidance memory in Wistar rats. Forty-five male Wistar rats (250 ± 20 g) were divided into 9 groups (n=5) and received intra-hippocampal injections of 3.0 nmol ghrelin and performed aerobic exercise training for 8 weeks. Control groups received the same volume of saline and diazepam as negative and positive control groups, respectively. Learning and memory were estimated using a shuttle box apparatus, and anxiety-like behavior was recorded by an elevated plus-maze test (EPM). Data were analyzed by ANOVA test, and p<0.05 was considered significant. Our findings showed that the combined effect of ghrelin and aerobic exercise improves the acquisition, consolidation, and retrieval of passive avoidance memory in Wistar rats. Furthermore, it is supposed that the ghrelin receiving group spent less time in open arms and fewer open arms entries compared with the control group (p<0.05). However, exercising Wistar rats spent more time in the open arm zone in comparison with the control group (p<0.05). The exercise + Ghrelin administration established reduced anxiety (p<0.05). The results of this study demonstrate that aerobic exercise contributes to an increase in the endogenous production of ghrelin, and physical activity alleviates anxiety-related behaviors induced by intra-hippocampal injection of ghrelin. In general, exercise and ghrelin can reduce anxiety and improve memory.

Keywords: anxiety, ghrelin, aerobic exercise, learning, passive avoidance memory

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Authors: Ani Kojoyan

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Linguistic studies have been investigating a connection between gender and linguistic development since 1970s. Scholars claim that gender differences in first and second language learning are socially constructed. Recent studies to language learning and gender reveal that second language acquisition is also a social phenomenon directly influencing one’s gender identity. Those responsible for designing language learning-teaching materials should be encouraged to understand the importance of and address the gender sensitivity accurately in textbooks. Writing or compiling a textbook is not an easy task; it requires strong academic abilities, patience, and experience. For a long period of time Armenia has been involved in the compilation process of a number of foreign language textbooks. However, there have been very few discussions or evaluations of those textbooks which will allow specialists to theorize that practice. The present paper focuses on the analysis of gender sensitivity issues and policy aspects involved in an EFL textbook. For the research the following material has been considered – “A Basic English Grammar: Morphology”, first printed in 2011. The selection of the material is not accidental. First, the mentioned textbook has been widely used in university teaching over years. Secondly, in Armenia “A Basic English Grammar: Morphology” has considered one of the most successful English grammar textbooks in a university teaching environment and served a source-book for other authors to compile and design their textbooks. The present paper aims to find out whether an EFL textbook is gendered in the Armenian teaching environment, and whether the textbook compilers are aware of gendered messages while compiling educational materials. It also aims at investigating students’ attitude toward the gendered messages in those materials. And finally, it also aims at increasing the gender sensitivity among book compilers and educators in various educational settings. For this study qualitative and quantitative research methods of analyses have been applied, the quantitative – in terms of carrying out surveys among students (45 university students, 18-25 age group), and the qualitative one – by discourse analysis of the material and conducting in-depth and semi-structured interviews with the Armenian compilers of the textbook (interviews with 3 authors). The study is based on passive and active observations and teaching experience done in a university classroom environment in 2014-2015, 2015-2016. The findings suggest that the discussed and analyzed teaching materials (145 extracts and examples) include traditional examples of intensive use of language and role-modelling, particularly, men are mostly portrayed as active, progressive, aggressive, whereas women are often depicted as passive and weak. These modeled often serve as a ‘reliable basis’ for reinforcing the traditional roles that have been projected on female and male students. The survey results also show that such materials contribute directly to shaping learners’ social attitudes and expectations around issues of gender. The applied techniques and discussed issues can be generalized and applied to other foreign language textbook compilation processes, since those principles, regardless of a language, are mostly the same.

Keywords: EFL textbooks, gender policy, gender sensitivity, qualitative and quantitative research methods

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Authors: Hanan K. Hassanein

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Task-based language teaching (TBLT) and focus on form instruction (FFI) methods were proven to improve quality and quantity of immediate language production. However, studies that compare between the effectiveness of the language production when using TBLT versus FFI are very little with results that are not consistent. Moreover, teaching Arabic using TBLT is a new field with few research that has investigated its application inside classrooms. Furthermore, to the best knowledge of the researcher, there are no prior studies that compared teaching Arabic as a foreign language in a classroom setting using computer-assisted task-based language teaching (CATBLT) with computer-assisted form focused language instruction (CAFFI). Accordingly, the focus of this presentation is to display CATBLT and CAFFI tools when teaching Arabic as a foreign language as well as demonstrate an experimental study that aims to identify whether or not CATBLT is a more effective instruction method. The effectiveness will be determined through comparing CATBLT and CAFFI in terms of accuracy, lexical complexity, and fluency of language produced by students. The participants of the study are 20 students enrolled in two intermediate-level Arabic as a foreign language classes. The experiment will take place over the course of 7 days. Based on a study conducted by Abdurrahman Arslanyilmaz for teaching Turkish as a second language, an in-house computer assisted tool for the TBLT and another one for FFI will be designed for the experiment. The experimental group will be instructed using the in-house CATBLT tool and the control group will be taught through the in-house CAFFI tool. The data that will be analyzed are the dialogues produced by students in both the experimental and control groups when completing a task or communicating in conversational activities. The dialogues of both groups will be analyzed to understand the effect of the type of instruction (CATBLT or CAFFI) on accuracy, lexical complexity, and fluency. Thus, the study aims to demonstrate whether or not there is an instruction method that positively affects the language produced by students learning Arabic as a foreign language more than the other.

Keywords: computer assisted language teaching, foreign language teaching, form-focused instruction, task based language teaching

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Authors: Da Kuang

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Resilience is a hotly debated topic in many research fields (e.g., engineering, ecology, society, psychology). In flood management studies, we are experiencing the paradigm shift from flood resistance to flood resilience. Flood resilience refers to tolerate flooding through adaptation or transformation. It is increasingly argued that our city as a social-ecological system holds the ability to learn from experience and adapt to flood rather than simply resist it. This research aims to investigate what kinds of adaptation knowledge the frequently flooded village learned from past experience and its advantages and limitations in coping with floods. The study area – Xinnongcun village, located in the west of Wuhan city, is a linear village and continuously suffered from both flash flood and drainage flood during the past 30 years. We have a field trip to the site in June 2017 and conducted semi-structured interviews with local residents. Our research summarizes two types of adaptation knowledge that people learned from the past floods. Firstly, at the village scale, it has formed a collective urban form which could help people live during both flood and dry season. All houses and front yards were elevated about 2m higher than the road. All the front yards in the village are linked and there is no barrier. During flooding time, people walk to neighbors through houses yards and boat to outside village on the lower road. Secondly, at individual scale, local people learned tacit knowledge of preparedness and emergency response to flood. Regarding the advantages and limitations, the adaptation knowledge could effectively help people to live with flood and reduce the chances of getting injuries. However, it cannot reduce local farmers’ losses on their agricultural land. After flood, it is impossible for local people to recover to the pre-disaster state as flood emerges during June and July will result in no harvest. Therefore, we argue that learning from past flood experience could increase people’s adaptive capacity. However, once the adaptive capacity cannot reduce people’s losses, it requires a transformation to a better regime.

Keywords: adaptation, flood resilience, tacit knowledge, transformation

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Authors: Meng-Fei Cheng, Wei Fneg

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New Taiwan high school standards emphasize employing scientific models and modeling practices in physics learning. However, to our knowledge. Few studies address how scientific models and modeling are approached in current science teaching, and they do not examine the views of scientific models portrayed in the textbooks. To explore the views of scientific models and modeling in textbooks, this study investigated the atomic unit in different textbook versions as an example and provided suggestions for modeling curriculum. This study adopted a quantitative analysis of qualitative data in the atomic units of four mainstream version of Taiwan high school physics textbooks. The models were further analyzed using five dimensions of the views of scientific models (nature of models, multiple models, purpose of the models, testing models, and changing models); each dimension had three levels (low, medium, high). Descriptive statistics were employed to compare the frequency of describing the five dimensions of the views of scientific models in the atomic unit to understand the emphasis of the views and to compare the frequency of the eight scientific models’ use to investigate the atomic model that was used most often in the textbooks. Descriptive statistics were further utilized to investigate the average levels of the five dimensions of the views of scientific models to examine whether the textbooks views were close to the scientific view. The average level of the five dimensions of the eight atomic models were also compared to examine whether the views of the eight atomic models were close to the scientific views. The results revealed the following three major findings from the atomic unit. (1) Among the five dimensions of the views of scientific models, the most portrayed dimension was the 'purpose of models,' and the least portrayed dimension was 'multiple models.' The most diverse view was the 'purpose of models,' and the most sophisticated scientific view was the 'nature of models.' The least sophisticated scientific view was 'multiple models.' (2) Among the eight atomic models, the most mentioned model was the atomic nucleus model, and the least mentioned model was the three states of matter. (3) Among the correlations between the five dimensions, the dimension of 'testing models' was highly related to the dimension of 'changing models.' In short, this study examined the views of scientific models based on the atomic units of physics textbooks to identify the emphasized and disregarded views in the textbooks. The findings suggest how future textbooks and curriculum can provide a thorough view of scientific models to enhance students' model-based learning.

Keywords: atomic models, textbooks, science education, scientific model

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Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

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Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

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Authors: Tsu-Yin Wu, Jenni Hoffman, Lydia McMurrows, Sarah Lally

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Recent events of the Flint Water Crisis and elevated lead levels in Detroit public school water have highlighted a specific public health disparity and shown the need for better education of healthcare providers on lead education. Identifying children and pregnant women with a high risk for lead poisoning and ensuring lead testing is completed is critical. The purpose of this study is to explore the impact of an educational intervention on knowledge and confidence levels among nursing students enrolled in the prelicensure Bachelor of Science in Nursing (BSN) and Registered Nurse to BSN program (R2B). The study used both quantitative and qualitative research methods to assess the impact of multi-modal pedagogy on knowledge and confidence of lead screening and prevention among prelicensure and R2B nursing students. The students received lead poisoning and prevention content in addition to completing an e-learning module developed by the Pediatric Environmental Health Specialty Units. A total of 115 students completed the pre-and post-test instrument that consisted of demographic, lead knowledge, and confidence items. Despite the increase of total knowledge, three dimensions of lead poisoning, and confidence from pre- to post-test scores for both groups, there was no statistical significance on the increase between prelicensure and R2B students. Thematic analysis of qualitative data showed five themes from participants' learning experiences: lead exposure, signs and symptoms of lead poisoning, screening and diagnosis, prevention, and policy and statewide issues. The study is limited by a small sample and participants recalling some correct answers from the pretest, thus, scoring higher on the post-test. The results contribute to the minimally existent literature examining a critical public health concern regarding lead health exposure and prevention education of nursing students. Incorporating such content area into the nursing curriculum is essential in ensuring that such public health disparities are mitigated.

Keywords: lead poisoning, emerging public health issue, community health, nursing edducation

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Authors: Rosa Figueroa, Christopher Flores

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Text categorization is the problem of assigning a new document to a set of predetermined categories, on the basis of a training set of free-text data that contains documents whose category membership is known. To train a classification model, it is necessary to extract characteristics in the form of tokens that facilitate the learning and classification process. In text categorization, the feature extraction process involves the use of word sequences also known as N-grams. In general, it is expected that documents belonging to the same category share similar features. The Smith-Waterman (SW) algorithm is a dynamic programming algorithm that performs a local sequence alignment in order to determine similar regions between two strings or protein sequences. This work explores the use of SW algorithm as an alternative to feature extraction in text categorization. The dataset used for this purpose, contains 2,610 annotated documents with the classes Obese/Non-Obese. This dataset was represented in a matrix form using the Bag of Word approach. The score selected to represent the occurrence of the tokens in each document was the term frequency-inverse document frequency (TF-IDF). In order to extract features for classification, four experiments were conducted: the first experiment used SW to extract features, the second one used unigrams (single word), the third one used bigrams (two word sequence) and the last experiment used a combination of unigrams and bigrams to extract features for classification. To test the effectiveness of the extracted feature set for the four experiments, a Support Vector Machine (SVM) classifier was tuned using 20% of the dataset. The remaining 80% of the dataset together with 5-Fold Cross Validation were used to evaluate and compare the performance of the four experiments of feature extraction. Results from the tuning process suggest that SW performs better than the N-gram based feature extraction. These results were confirmed by using the remaining 80% of the dataset, where SW performed the best (accuracy = 97.10%, weighted average F-measure = 97.07%). The second best was obtained by the combination of unigrams-bigrams (accuracy = 96.04, weighted average F-measure = 95.97) closely followed by the bigrams (accuracy = 94.56%, weighted average F-measure = 94.46%) and finally unigrams (accuracy = 92.96%, weighted average F-measure = 92.90%).

Keywords: comorbidities, machine learning, obesity, Smith-Waterman algorithm

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Authors: M. Cabrera, W. Tizani, J. Ninic, F. Wang

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Open section beam to concrete-filled tubular column structures has been increasingly utilized in construction over the past few decades due to their enhanced structural performance, as well as economic and architectural advantages. However, the use of this configuration in construction is limited due to the difficulties in connecting the structural members as there is no access to the inner part of the tube to install standard bolts. Blind-bolted systems are a relatively new approach to overcome this limitation as they only require access to one side of the tubular section to tighten the bolt. The performance of these connections in concrete-filled steel tubular sections remains uncharacterized due to the complex interactions between concrete, bolt, and steel section. Over the last years, research in structural performance has moved to a more sophisticated and efficient approach consisting of machine learning algorithms to generate metamodels. This method reduces the need for developing complex, and computationally expensive finite element models, optimizing the search for desirable design variables. Metamodels generated by a data fusion approach use numerical and experimental results by combining multiple models to capture the dependency between the simulation design variables and connection performance, learning the relations between different design parameters and predicting a given output. Fully characterizing this connection will transform high-rise and multistorey construction by means of the introduction of design guidance for moment-resisting blind-bolted connections, which is currently unavailable. This paper presents a review of the steps taken to develop metamodels generated by means of artificial neural network algorithms which predict the connection stress and stiffness based on the design parameters when using Extended Hollo-Bolt blind bolts. It also provides consideration of the failure modes and mechanisms that contribute to the deformability as well as the feasibility of achieving blind-bolted rigid connections when using the blind fastener.

Keywords: blind-bolted connections, concrete-filled tubular structures, finite element analysis, metamodeling

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Authors: Z. Gharaee

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Recognizing actions performed by others is important in our daily lives since it is necessary for communicating with others in a proper way. We perceive an action by observing the kinematics of motions involved in the performance. We use our experience and concepts to make a correct recognition of the actions. Although building the action concepts is a life-long process, which is repeated throughout life, we are very efficient in applying our learned concepts in analyzing motions and recognizing actions. Experiments on the subjects observing the actions performed by an actor show that an action is recognized after only about two hundred milliseconds of observation. In this study, hierarchical action recognition architecture is proposed by using growing grid layers. The first-layer growing grid receives the pre-processed data of consecutive 3D postures of joint positions and applies some heuristics during the growth phase to allocate areas of the map by inserting new neurons. As a result of training the first-layer growing grid, action pattern vectors are generated by connecting the elicited activations of the learned map. The ordered vector representation layer receives action pattern vectors to create time-invariant vectors of key elicited activations. Time-invariant vectors are sent to second-layer growing grid for categorization. This grid creates the clusters representing the actions. Finally, one-layer neural network developed by a delta rule labels the action categories in the last layer. System performance has been evaluated in an experiment with the publicly available MSR-Action3D dataset. There are actions performed by using different parts of human body: Hand Clap, Two Hands Wave, Side Boxing, Bend, Forward Kick, Side Kick, Jogging, Tennis Serve, Golf Swing, Pick Up and Throw. The growing grid architecture was trained by applying several random selections of generalization test data fed to the system during on average 100 epochs for each training of the first-layer growing grid and around 75 epochs for each training of the second-layer growing grid. The average generalization test accuracy is 92.6%. A comparison analysis between the performance of growing grid architecture and self-organizing map (SOM) architecture in terms of accuracy and learning speed show that the growing grid architecture is superior to the SOM architecture in action recognition task. The SOM architecture completes learning the same dataset of actions in around 150 epochs for each training of the first-layer SOM while it takes 1200 epochs for each training of the second-layer SOM and it achieves the average recognition accuracy of 90% for generalization test data. In summary, using the growing grid network preserves the fundamental features of SOMs, such as topographic organization of neurons, lateral interactions, the abilities of unsupervised learning and representing high dimensional input space in the lower dimensional maps. The architecture also benefits from an automatic size setting mechanism resulting in higher flexibility and robustness. Moreover, by utilizing growing grids the system automatically obtains a prior knowledge of input space during the growth phase and applies this information to expand the map by inserting new neurons wherever there is high representational demand.

Keywords: action recognition, growing grid, hierarchical architecture, neural networks, system performance

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Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: Ahmet Kurnaz, Eyup Yurt, Ümit Çiftci

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In this study, too, an attempt was made to reveal the place and effects of information technologies on the lives and education of gifted children based on the views of gifted. To this end, the effects of information technologies on gifted are general skills, technology use, academic and social skills, and cooperative and personal skills were investigated. These skills were explored depending on whether or not gifted had their own computers, had internet connection at home, or how often they use the internet, average time period they spent at the computer, how often they played computer games and their use of social media. The study was conducted using the screening model with a quantitative approach. The sample of the study consisted of 129 gifted attending 5-12th classes in 12 provinces in different regions of Turkey. 64 of the participants were female while 65 were male. The research data were collected using the using computer of gifted and information technologies (UCIT) questionnaire which was developed by the researchers and given its final form after receiving expert view. As a result of the study, it was found that UCIT use improved foreign language speaking skills of gifted, enabled them to get to know and understand different cultures, and made use of computer and information technologies while they study. At the end of the study these result were obtained: Gifted have positive idea using computer and communication technology. There are differences whether using the internet about the ideas UCIT. But there are not differences whether having computer, inhabited city, grade level, having internet at home, daily and weekly internet usage durations, playing the computer and internet game, having Facebook and Twitter account about the UCIT. UCIT contribute to the development of gifted vocabulary, allows knowing and understand different cultures, developing foreign language speaking skills, gifted do not give up computer when they do their homework, improve their reading, listening, understanding and writing skills in a foreign language. Gifted children want to have transition to the use of tablets in education. They think UCIT facilitates doing their homework, contributes learning more information in a shorter time. They'd like to use computer-assisted instruction programs at courses. They think they will be more successful in the future if their computer skills are good. But gifted students prefer teacher instead of teaching with computers and they said that learning can be run from home without going to school.

Keywords: gifted, using computer, communication technology, information technologies

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Authors: M. S. Maglasang, S. O. Palacio, L. P. Ogdoc

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Business Process Outsourcing has been one of the fastest growing and emerging industry in the Philippines today. Unlike most of the contact service centers, more popularly known as "call centers", The BPO Industry’s primary outsourced service is performing audits of the global clients' logistics. As a service industry, manpower is considered as the most important yet the most expensive resource in the company. Because of this, there is a need to maximize the human resources so people are effectively and efficiently utilized. The main purpose of the study is to optimize the current manpower resources through effective distribution and assignment of different types of bills to the different skill-level of data entry operators. The assignment model parameters include the average observed time matrix gathered from through time study, which incorporates the learning curve concept. Subsequently, a simulation model was made to duplicate the arrival rate of demand which includes the different batches and types of bill per day. Next, a mathematical linear programming model was formulated. Its objective is to minimize direct labor cost per bill by allocating the different types of bills to the different skill-levels of operators. Finally, a hypothesis test was done to validate the model, comparing the actual and simulated results. The analysis of results revealed that the there’s low utilization of effective capacity because of its failure to determine the product-mix, skill-mix, and simulated demand as model parameters. Moreover, failure to consider the effects of learning curve leads to overestimation of labor needs. From 107 current number of operators, the proposed model gives a result of 79 operators. This results to an increase of utilization of effective capacity to 14.94%. It is recommended that the excess 28 operators would be reallocated to the other areas of the department. Finally, a manpower capacity planning model is also recommended in support to management’s decisions on what to do when the current capacity would reach its limit with the expected increasing demand.

Keywords: optimization modelling, linear programming, simulation, time and motion study, capacity planning

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Authors: Aaidha Hammad

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The age a child to be exposed to a second language is a controversial issue in communities such as the Maldives where English is taught as a second language. It has been observed that different stakeholders have different viewpoints towards the issue. Some believe that the earlier children are exposed to a second language, the better they learn, while others disagree with the notion. Hence, this case study investigates whether children learn a second language better when they are exposed at an earlier age or not. The spoken and written data collected confirm that earlier exposure helps in mastering the sound pattern and speaking fluency with more native-like accent, while a later age is better for learning more abstract and concrete aspects such as grammar and syntactic rules.

Keywords: age, fluency, second language acquisition, development of language skills

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Authors: Ezil Sam Leni, Shalen S.

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Transport service monitoring and upkeep are essential components of smart city initiatives. The main risks to the relevant departments and authorities are the ever-increasing vehicular traffic and the conditions of the roads. In India, the economy is greatly impacted by the road transport sector. In 2021, the Ministry of Road Transport and Highways Transport, Government of India, produced a report with statistical data on traffic accidents. The data included the number of fatalities, injuries, and other pertinent criteria. This study proposes a distributed infrastructure for the monitoring, detection, and reporting of potholes to the appropriate authorities. In a distributed environment, the nodes are the edge devices, and local edge servers, and global servers. The edge devices receive the initial model to be employed from the global server. The YOLOv8 model for pothole detection is used in the edge devices. The edge devices run the pothole detection model, gather the pothole images on their path, and send the updates to the nearby edge server. The local edge server selects the clients for its aggregation process, aggregates the model updates and sends the updates to the global server. The global server collects the updates from the local edge servers, performs aggregation and derives the updated model. The updated model has the information about the potholes received from the local edge servers and notifies the updates to the local edge servers and concerned authorities for monitoring and maintenance of road conditions. The entire process is implemented in FedCV distributed environment with the implementation using the client-server model and aggregation entities. After choosing the clients for its aggregation process, the local edge server gathers the model updates and transmits them to the global server. After gathering the updates from the regional edge servers, the global server aggregates them and creates the updated model. Performance indicators and the experimentation environment are assessed, discussed, and presented. Accelerometer data may be taken into consideration for improved performance in the future development of this study, in addition to the images captured from the transportation routes.

Keywords: federated Learning, pothole detection, distributed framework, federated averaging

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Authors: Abimbola Akintounde

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Supporting the social and emotional needs of young adolescents has become an emergent concern for schools around the world. Yet educators remain in a dilemma regarding the optimum approach for integrating social and emotional learning (SEL) into their content area instruction. The purpose of this study was to explore the perception of secondary students regarding their schoolwide SEL interventions. Twenty-four International Baccalaureate students in a final year mathematics course at an American Public Secondary School near Washington D. C. were randomly selected for participation in this study via an online electronic survey. The participants in this study used Likert-scale items to rate the effectiveness of the socio-emotional and character development programs being implemented at their schools. Respondents also ranked their preferred mode of delivery of social and emotional support programs. About 71% of the teenagers surveyed preferred SEL support rendered via interactive team-building activities and games, 42% of the high school students in the study ranked focus group discussions as their preferred format for SEL interventions, while only 13% of the respondents in the study regarded lectures and presentations as their preferred mode of SEL delivery. About one-fourth of the study participants agreed that explicit instruction was critical to enhancing students’ wellness, 79% agreed that SEL programs should foster less teacher talk, while 88% of the students indicated that student engagement was critical to their mental health. Eighty percent of the teenagers surveyed decried that the focus of their school-wide social and emotional programs was poorly prioritized. About two-thirds of the students agreed that social justice and equity issues should be embedded in their schools’ advisory programs. More than half of the respondents agitated for strategies for managing stress and their school workload. About 54% of the respondents also clamored for SEL programs that reinforce emotion regulation and coping strategies for anxiety. Based on the findings of this study, recommendations were proffered for best practices in the design and implementation of effective learner-friendly social and emotional development interventions.

Keywords: SEL, math anxiety, student support, emotion regulation, social awareness, self awareness, self management, relationship building

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Authors: Li Xiaoge, Wang Chaodong

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We introduce the first Chinese Coreference Resolution Model based on BERT (CCRM-BERT) and show that it significantly outperforms all previous work. The key idea is to consider the features of the mention, such as part of speech, width of spans, distance between spans, etc. And the influence of each features on the model is analyzed. The model computes mention embeddings that combine BERT with features. Compared to the existing state-of-the-art span-ranking approach, our model significantly improves accuracy on the Chinese OntoNotes benchmark.

Keywords: BERT, coreference resolution, deep learning, nature language processing

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Authors: Essam Al Daoud

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Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records.

Keywords: gradient boosting, XGBoost, LightGBM, CatBoost, home credit

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Authors: Michael Wagner, Philipp Steinbauer, Andrea Katharina Lietz, Alexander Hoffelner, Johannes Fessler

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Background: Cardiopulmonary resuscitations are stressful situations in which vital decisions must be made within seconds. Lack of routine due to the infrequency of pediatric emergencies can lead to serious medical and communication errors. Virtual reality can fundamentally change the way simulation training is conducted in the future. It appears to be a useful learning tool for technical and non-technical skills. It is important to investigate the use of VR in providing a strong sense of presence within simulations. Methods: In this randomized study, we will enroll doctors and medical students from the Medical University of Vienna, who will receive learning material regarding the resuscitation of a one-year-old child. The study will be conducted in three phases. In the first phase, 20 physicians and 20 medical students from the Medical University of Vienna will be included. They will perform simulation-based training with a standardized scenario of a critically ill child with a hypovolemic shock. The main goal of this phase is to establish a baseline for the following two phases to generate comparative values regarding cognitive load and stress. In phase 2 and 3, the same participants will perform the same scenario in a VR setting. In both settings, on three set points of progression, one of three predefined events is triggered. For each event, three different stress levels (easy, medium, difficult) will be defined. Stress and cognitive load will be analyzed using the NASA Task Load Index, eye-tracking parameters, and heart rate. Subsequently, these values will be compared between VR training and traditional simulation-based training. Hypothesis: We hypothesize that the VR training and the traditional training groups will not differ in physiological response (cognitive load, heart rate, and heart rate variability). We further assume that virtual reality training can be used as cost-efficient additional training. Objectives: The aim of this study is to measure cognitive load and stress level during a real-life simulation training and compare it with VR training in order to show that VR training evokes the same physiological response and cognitive load as real-life simulation training.

Keywords: virtual reality, cognitive load, simulation, adaptive virtual reality training

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Authors: Nandu C. Nair, Kamal Bijlani

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This paper proposes a concept-based assessment to track the performance of the students. The idea behind this approach is to map the exam questions with the concepts learned in the course. So at the end of the course, each student will know how well he learned each concept. This system will give a self assessment for the students as well as instructor. By analyzing the score of all students, instructor can decide some concepts need to be teaching again or not. The system’s efficiency is proved using three courses from M-tech program in E-Learning technologies and results show that the concept-wise assessment improved the score in final exam of majority students on various courses.

Keywords: assessment, concept, examination, question, score

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Authors: Sisira Edirippulige

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Background: Digital health education and training (DHET) is imperative for preparing current and future clinicians to work competently in digitally enabled environments. Despite rapid integration of digital health in modern health services, systematic education and training opportunities for health workers is still lacking. Objectives: This study aimed to investigate healthcare professionals’ perspectives and expectations regarding the knowledge, skills and competency needs in digital health for current and future healthcare workforce. Methods: A qualitative study design with semi-structured individual interviews was employed. A purposive sample method was adopted to collect relevant information from the health workers. Inductive thematic analysis was used to analyse data. Interviews were audio-recorded and transcribed verbatim. Consolidated Criteria for Reporting Qualitative Research (COREQ) was followed when we reported this study. Results: Two themes emerged while analysing the data: (1) what to teach in DHET and (2) how to teach DHET. Overall, healthcare professionals agreed that DHET is important for preparing current and future clinicians for working competently in digitally enabled environments. Knowledge relating to what is digital health, types of digital health, use of technology and human factors in digital health were considered as important to be taught in DHET. Skills relating to digital health consultations, clinical information system management and remote monitoring were considered important to be taught. Blended learning which combined e-learning and classroom-based teaching, simulation sessions and clinical rotations were suggested by healthcare professionals as optimal approaches to deliver the above-mentioned content. Conclusions: This study is the first of its kind to investigate health professionals’ perspectives and expectations relating to the knowledge, skills and competency needs in digital health for current and future healthcare workforce. Healthcare workers are keen to acquire relevant knowledge, skills and competencies related to digital health. Different modes of education delivery is of interest to fit in with busy schedule of health workers.

Keywords: digital health, telehealth, telemedicine, education, curriculum

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Authors: Ayesha Sanjana Kawser Parsha

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S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

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Authors: A. A. Altalbe, N. W Bergmann

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There has been significant recent interest in on-line learning, as well as considerable work on developing technologies for virtual laboratories for engineering students. After reviewing the state-of-the-art of virtual laboratories, this paper steps back from the technology issues to look in more detail at the pedagogical issues surrounding virtual laboratories, and examines the role of gathering student feedback in the development of such laboratories. The main contribution of the paper is a set of student surveys before and after a prototype deployment of a simulation laboratory tool, and the resulting analysis which leads to some tentative guidelines for the design of virtual engineering laboratories.

Keywords: engineering education, elearning, electrical engineering, virtual laboratories

Procedia PDF Downloads 361

Authors: Wai Ha Leung

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Mother tongue language (MTL) curriculum is core to school education in most countries/regions' school curriculum. Through mother tongue language learning, students are expected to enhance their understanding of the nation's culture and foster the sense of cultural and ethnic identity. However, MTL education in Hong Kong is complicated by the colonial history. This study examines Hong Kong Chinese language teachers' perceptions of MTL education, and the implication on MTL curriculum development. The questionnaire was administrated to 97 teachers, and interviews were carried out on 17 teachers. Usually, MTL is both the tool with which knowledge and skills are taught and learned and the vehicle for students to learn about the traditions of the countries' literature and culture. In Hong Kong, 95% of the population is of Chinese descent. Traditionally, education in China was a mixture of philosophy, history, politics and literacy. Chinese as an MTL subject in pre-colonial Hong Kong has always been assigned the mission of developing students' cultural identity in addition to the development of linguistic proficiency. During the colonial period, the Chinese Language curriculum shifted to be more language skills based with less emphasis on Chinese culture and moral education. After the sovereignty of Hong Kong was returned to China in 1997, although a new curriculum was implemented in 2002, teaching and learning in school as well as public examinations seem to be remaining language skills oriented instead of culturally based. This deviation from the trend of both Chinese traditional education and global mother tongue language education makes some Chinese language teachers feel confused. In addition, there is comment that in general Hong Kong students' Chinese language proficiency is becoming weaker and weaker in recent years. Thus, effectiveness of the skills oriented language curriculum has come under question. How a language teacher views the aims and objectives of the language subject he or she is teaching has a direct effect on the curriculum delivery and pedagogies used. It is, therefore, important to investigate what is the language teachers' perception of MTL education, and whether the current school curriculum can meet the teachers' expectation as well as achieve the aims of MTL education. Given this context, this study explored the views of Hong Kong Chinese language teachers on MTL education. The data indicate that teachers showed a strong resentment towards the current curriculum. Results may have implications on mother tongue language curriculum development.

Keywords: Chinese language education, curriculum development, mother tongue language education, teachers' perception

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Authors: Reham Banwair, Lana Alshehri, Sara Hadrawi

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The purpose of this project is to identify user satisfaction with the AI functions on Snapchat, in order to generate improvement proposals that allow its development within the app. To achieve this, a qualitative analysis was carried out through interviews to people who usually use the application, revealing their satisfaction or dissatisfaction with the usefulness of the AI. In addition, the background of the company and its introduction in these algorithms were analyzed. Furthermore, the characteristics of the three main functions of AI were explained: identify songs, solve mathematical problems, and recognize plants. As a result, it was obtained that 50% still do not know the characteristics of AI, 50% still believe song recognition is not always correct, 41.7% believe that math problems are usually accurate and 91.7% believes the plant detection tool is working properly.

Keywords: artificial intelligence, scanning, Snapchat, machine learning

Procedia PDF Downloads 137

Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

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Authors: Mustafa Aydın, Ali Murat Sunbul

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Hierarchical or nested data structures usually are seen in many research areas. Especially, in the field of education, if we examine most of the studies, we can see the nested structures. Students in classes, classes in schools, schools in cities and cities in regions are similar nested structures. In a hierarchical structure, students being in the same class, sharing the same physical conditions and similar experiences and learning from the same teachers, they demonstrate similar behaviors between them rather than the students in other classes.

Keywords: hierarchical linear modeling, nested data, hierarchical structure, data structure

Procedia PDF Downloads 654