Search results for: L2 language learning

Authors: Nino Gogatishvili, Davit Kvernadze, Giorgi Japharidze

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Background and aims: Landau Kleffner syndrome (LKS) is a rare disorder with epileptic seizures and acquired aphasia. It usually starts in initially healthy children. The first symptoms are language regression and behavioral disturbances, and the sleep EEG reveals abnormal epileptiform activity. The aim was to discuss the efficacy of Clobazam for Landau Kleffner syndrome. Case report: We report a case of an 11-year-old boy with an uneventful pregnancy and delivery. He began to walk at 11 months and speak with simple phrases at the age of 2,5 years. At the age of 18 months, he had febrile convulsions; at the age of 5 years, the parents noticed language regression, stuttering, and serious behavioral dysfunction, including hyperactivity, temper outbursts. The epileptic seizure was not noticed. MRI was without any abnormality. Neuropsychological testing revealed verbal auditory agnosia. Sleep EEG showed abundant left fronto-temporal spikes, reaching over 85% during non-rapid eye movement sleep (non-REM sleep). Treatment was started with Clobazam. After ten weeks, EEG was improved. Stuttering and behavior also improved. Results: Since the start of Clobazam treatment, stuttering and behavior improved. Now, he is 11 years old, without antiseizure medication. Sleep EEG shows fronto-temporal spikes on the left side, over 10-49 % of non-REM sleep, bioccipital spikes, and slow-wave discharges and spike-waves. Conclusions: This case provides further support for the efficacy of Clobazam in patients with LKS.

Keywords: Landau-Kleffner syndrome, antiseizure medication, stuttering, aphasia

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

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Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Bruna Azzari Puga, Richard Roe, Andre Pagani de Souza

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abstract outlines a proposed study to examine how and to what extent interactive, experiential, learner centered methodology develops learning of basic civic and democratic competencies among young children. It stems from the Literacy and Law course taught at Georgetown University Law Center in Washington, DC, since 1998. Law students, trained in best literacy practices and legal cases affecting literacy development, read “law related” children’s books and engage in interactive and extension activities with emerging readers. The law students write a monthly journal describing their experiences and a final paper: a conventional paper or a children’s book illuminating some aspect of literacy and law. This proposal is based on the recent adaptation of Literacy and Law to Brazil at Mackenzie Presbyterian University in São Paulo in three forms: first, a course similar to the US model, often conducted jointly online with Brazilian and US law students; second, a similar course that combines readings of children’s literature with activity based learning, with law students from a satellite Mackenzie campus, for young children from a vulnerable community near the city; and third, a course taught by law students at the main Mackenzie campus for 4th grade students at the Mackenzie elementary school, that is wholly activity and discourse based. The workings and outcomes of these courses are well documented by photographs, reports, lesson plans, and law student journals. The authors, faculty who teach the above courses at Mackenzie and Georgetown, observe that literacy, broadly defined as cognitive and expressive development through reading and discourse-based activities, can be influential in developing democratic civic skills, identifiable by explicit civic competencies. For example, children experience justice in the classroom through cooperation, creativity, diversity, fairness, systemic thinking, and appreciation for rules and their purposes. Moreover, the learning of civic skills as well as the literacy skills is enhanced through interactive, learner centered practices in which the learners experience literacy and civic development. This study will develop rubrics for individual and classroom teaching and supervision by examining 1) the children’s books and students diaries of participating law students and 2) the collection of photos and videos of classroom activities, and 3) faculty and supervisor observations and reports. These rubrics, and the lesson plans and activities which are employed to advance the higher levels of performance outcomes, will be useful in training and supervision and in further replication and promotion of this form of teaching and learning. Examples of outcomes include helping, cooperating and participating; appreciation of viewpoint diversity; knowledge and utilization of democratic processes, including due process, advocacy, individual and shared decision making, consensus building, and voting; establishing and valuing appropriate rules and a reasoned approach to conflict resolution. In conclusion, further development and replication of the learner centered literacy and law practices outlined here can lead to improved qualities of democratic teaching and learning supporting mutual respect, positivity, deep learning, and the common good – foundation qualities of a sustainable world.

Keywords: democracy, law, learner-centered, literacy

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Authors: L. R. Valencia Pérez, A. Morita Alexander, Peña A. Juan Manuel, A. Lamadrid Álvarez

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This research shows the reflection and identification of explanatory variables and their relationships between different variables that are involved with educational technology, all of them encompassed in macro-concepts which are: cognitive inequality, economy, food and language; These will give the guideline to have a more detailed knowledge of educational systems, the communication and equipment, the physical space and the teachers; All of them interacting with each other give rise to what is called educational technology management. These elements contribute to have a very specific knowledge of the equipment of communications, networks and computer equipment, systems and content repositories. This is intended to establish the importance of knowing a global environment in the transfer of knowledge in poor countries, so that it does not diminish the capacity to be authentic and preserve their cultures, their languages or dialects, their hierarchies and real needs; In short, to respect the customs of different towns, villages or cities that are intended to be reached through the use of internationally agreed professional educational technologies. The methodology used in this research is the analytical - descriptive, which allows to explain each of the variables, which in our opinion must be taken into account, in order to achieve an optimal incorporation of the educational technology in a model that gives results in a medium term. The idea is that in an encompassing way the concepts will be integrated to others with greater coverage until reaching macro concepts that are of national coverage in the countries and that are elements of conciliation in the different federal and international reforms. At the center of the model is the educational technology which is directly related to the concepts that are contained in factors such as the educational system, communication and equipment, spaces and teachers, which are globally immersed in macro concepts Cognitive inequality, economics, food and language. One of the major contributions of this article is to leave this idea under an algorithm that allows to be as unbiased as possible when evaluating this indicator, since other indicators that are to be taken from international preference entities like the OECD in the area of education systems studied, so that they are not influenced by particular political or interest pressures. This work opens the way for a relationship between involved entities, both conceptual, procedural and human activity, to clearly identify the convergence of their impact on the problem of education and how the relationship can contribute to an improvement, but also shows possibilities of being able to reach a comprehensive education reform for all.

Keywords: relationships macro-concepts, cognitive inequality, economics, alimentation and language

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Ayse Tarhan, Mustafa Ilkan, Mohammad Karimzadeh

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Science was considered as part of philosophy in ancient Greece. By the nineteenth century, it was understood that philosophy was very inclusive and that social and human sciences such as literature, history, and psychology should be separated and perceived as an autonomous branch of science. The computer was also first seen as a tool of mathematical science. Over time, computer science has grown by encompassing every area in which technology exists, and its growth compelled the division of computer science into different disciplines, just as philosophy had been divided into different branches of science. Now there is almost no branch of science in which computers are not used. One of the newer autonomous disciplines of computer science is digital humanities, and one of the areas of digital humanities is literature. The material of literature is words, and thanks to the software tools created using computer programming languages, data that a literature researcher would need months to complete, can be achieved quickly and objectively. In this article, three different tools that literary researchers can use in their work will be introduced. These studies were created with the computer programming languages Python and R and brought to the world of literature. The purpose of introducing the aforementioned studies is to set an example for the development of special tools or programs on Ottoman language and literature in the future and to support such initiatives. The first example to be introduced is the Stylometry tool developed with the R language. The other is The Metrical Tool, which is used to measure data in poems and was developed with Python. The latest literature analysis tool in this article is Voyant Tools, which is a multifunctional and easy-to-use tool.

Keywords: DH, literature, information technologies, stylometry, the metrical tool, voyant tools

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Authors: Sandesh Achar

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Artificial intelligence is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remain a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights excellent investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: artificial intelligence, cloud computing, deep learning, machine learning, internet of things

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Authors: Qin Long, Li Xiaoge

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The Knowledge Graphs have now become a new form of knowledge representation. However, there is no consensus in regard to a plausible and definition of entities and relationships in the domain-specific knowledge graph. Further, in conjunction with several limitations and deficiencies, various domain-specific entities and relationships recognition approaches are far from perfect. Specifically, named entity recognition in Chinese domain is a critical task for the natural language process applications. However, a bottleneck problem with Chinese named entity recognition in new domains is the lack of annotated data. To address this challenge, a domain distant supervised named entity recognition framework is proposed. The framework is divided into two stages: first, the distant supervised corpus is generated based on the entity linking model of graph attention neural network; secondly, the generated corpus is trained as the input of the distant supervised named entity recognition model to train to obtain named entities. The link model is verified in the ccks2019 entity link corpus, and the F1 value is 2% higher than that of the benchmark method. The re-pre-trained BERT language model is added to the benchmark method, and the results show that it is more suitable for distant supervised named entity recognition tasks. Finally, it is applied in the computer field, and the results show that this framework can obtain domain named entities.

Keywords: distant named entity recognition, entity linking, knowledge graph, graph attention neural network

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Authors: Ayşegül Çabuk, Mine Işıksal

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Mathematics is a sense-making activity so that it requires meaningful learning. Hence based on this idea, meaningful mathematical connections are necessary to learn mathematics. At that point, the major question has become that which educational methods can provide opportunities to provide mathematical connections and to understand mathematics. The amalgam of reasoning and proof can be the one of the methods that creates opportunities to learn mathematics in a meaningful way. However, even if reasoning and proof should be included from prekindergarten to grade 12, studies in literature generally include secondary school students and pre-service mathematics teachers. With the light of the idea that the amalgam of reasoning and proof has significant effect on middle school students' mathematical learning, this study aims to investigate middle grade students' tendencies while reasoning the correctness of statements related to numbers. The sample included 272 middle grade students, specifically 69 of them were sixth grade students (25.4%), 101 of them were seventh grade students (37.1%) and 102 of them were eighth grade students (37.5%). Data was gathered through an achievement test including 2 essay types of problems about algebra. The answers of two items were analyzed both quantitatively and qualitatively in terms of students' solutions strategies while reasoning the correctness of the statements. Similar on the findings in the literature, most of the students, in all grade levels, used numerical examples to judge the statements. Moreover the results also showed that the majority of these students appear to believe that providing one or more selected examples is sufficient to show the correctness of the statement. Hence based on the findings of the study, even students in earlier ages have proving and reasoning abilities their reasoning's generally based on the empirical evidences. Therefore, it is suggested that examples and example-based reasoning can be a fundamental role on to generate systematical reasoning and proof insight in earlier ages.

Keywords: reasoning, mathematics learning, middle grade students

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Authors: A. Eiguren-Munitis, N. Berasategi, J. M. Correa

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Changes in family structures, immigration, economic crisis, among others, hinder the connection between different generations. This situation gives rise to a greater lack of social protection of the groups in vulnerable situations, such as the elderly and children. There is a growing need to search for shared spaces where different generations manage to break negative stereotypes and interact with each other. The school environment provides a favourable context in which the approach of different generations can be worked on. The intergenerational experiences that take place within the school context help to introduce the educational ideology for a lifetime. This induces bilateral learning, which encourages citizen participation. For this reason, the general objective of this research is to deepen the impact that intergenerational experiences have on participating students. The research is carried out based on mixed methods. The qualitative and quantitative evaluation included pre-test and post-test questionnaires (n=148) and group interviews (n=43). The results indicate that the intergenerational experiences influence different levels, on the one hand, help to promote school motivation and on the other hand, help to reduce negative stereotypes towards older people thus contributing to greater social cohesion.

Keywords: intergenerational learning, school, stereotypes, social cohesion

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Authors: Muhammad Ramzan

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STEM Education and Critical Thinking Skills are important 21st-century skills. STEM Education is necessary to promote secondary school students’ critical thinking skills. These skills are critical for teachers to respond to students. Pakistan is in the preliminary stages of integrating STEM Education in institutions like other developing countries. Unfortunately, most secondary school students in Pakistan are unaware of STEM Education and teachers are not applying critical thinking skills in classrooms. The study's objectives mainly deal with; to identify the importance of STEM Education in the teaching-learning process; to find out the factors affecting critical thinking skills that can develop interest in students in STEM Education and suggestions on how to improve critical thinking skills in students regarding STEM Education. This study was descriptive. The population of the study was secondary school students. Data was collected from 200 secondary school students through a questionnaire. The research results show that critical thinking skills develop interest in students towards STEM Education.

Keywords: STEM education, teachers, students, critical thinking skills, teaching and learning process

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Authors: Ahmed Abdelrahman, Ahmed Abdelraheem

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There are many essential factors affecting the integration of information and communication technology (ICT) into teaching and learning, including technology infrastructure, institutional support, professional development, and faculty members’ beliefs regarding ICT integration. The present research project investigated the current status of integrating ICT into teaching and learning at Sultan Qaboos University (SQU). A sample of 220 faculty members from six different colleges and four administrators from the Center of Educational Technology (CET) and the Center for Information Systems (CIS) at SQU in Oman were chosen, and quantitative, qualitative design using a semi-structured questionnaire, interviews and checklists were employed. The findings show that SQU had a high availability of ICT infrastructure in terms of hardware, software, and support services, as well as adequate computer labs for educational purposes. However, the results also indicated that, although SQU provided a series of professional development workshops related to using ICT in teaching, few faculty members were interested. Furthermore, the finding indicated that the degree of ICT integration into teaching at SQU was at a medium level.

Keywords: information and communication technology, integration, professional development, teaching

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Brenda Ravenscroft, Ulemu Luhanga, Bev King

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A comprehensive evaluation of online learning needs to include a blend of educational design, technology use, and online instructional practices that integrate technology appropriately for developing and delivering quality online courses. Research shows that classroom-based evaluation tools do not adequately capture the dynamic relationships between content, pedagogy, and technology in online courses. Furthermore, studies suggest that using classroom evaluations for online courses yields lower than normal scores for instructors, and may affect faculty negatively in terms of administrative decisions. In 2014, the Faculty of Arts and Science at Queen’s University responded to this evidence by seeking an alternative to the university-mandated evaluation tool, which is designed for classroom learning. The Faculty is deeply engaged in e-learning, offering large variety of online courses and programs in the sciences, social sciences, humanities and arts. This paper describes the process by which a new student survey instrument for online courses was developed and piloted, the methods used to analyze the data, and the ways in which the instrument was subsequently adapted based on the results. It concludes with a critical reflection on the challenges of evaluating e-learning. The Student Evaluation of Online Teaching Effectiveness (SEOTE), developed by Arthur W. Bangert in 2004 to assess constructivist-compatible online teaching practices, provided the starting point. Modifications were made in order to allow the instrument to serve the two functions required by the university: student survey results provide the instructor with feedback to enhance their teaching, and also provide the institution with evidence of teaching quality in personnel processes. Changes were therefore made to the SEOTE to distinguish more clearly between evaluation of the instructor’s teaching and evaluation of the course design, since, in the online environment, the instructor is not necessarily the course designer. After the first pilot phase, involving 35 courses, the results were analyzed using Stobart's validity framework as a guide. This process included statistical analyses of the data to test for reliability and validity, student and instructor focus groups to ascertain the tool’s usefulness in terms of the feedback it provided, and an assessment of the utility of the results by the Faculty’s e-learning unit responsible for supporting online course design. A set of recommendations led to further modifications to the survey instrument prior to a second pilot phase involving 19 courses. Following the second pilot, statistical analyses were repeated, and more focus groups were used, this time involving deans and other decision makers to determine the usefulness of the survey results in personnel processes. As a result of this inclusive process and robust analysis, the modified SEOTE instrument is currently being considered for adoption as the standard evaluation tool for all online courses at the university. Audience members at this presentation will be stimulated to consider factors that differentiate effective evaluation of online courses from classroom-based teaching. They will gain insight into strategies for introducing a new evaluation tool in a unionized institutional environment, and methodologies for evaluating the tool itself.

Keywords: evaluation, online courses, student survey, teaching effectiveness

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how to achieve knowledge understanding and prediction (Situation Awareness (SA)) for multiple-agents conducting searching activity using Bayesian inferential reasoning and learning. Bayesian Belief Network was used to monitor agents' knowledge about their environment, and cases are recorded for the network training using expectation-maximisation or gradient descent algorithm. The well trained network will be used for decision making and environmental situation prediction. Forest fire searching by multiple UAVs was the use case. UAVs are tasked to explore a forest and find a fire for urgent actions by the fire wardens. The paper focused on two problems: (i) effective agents’ path planning strategy and (ii) knowledge understanding and prediction (SA). The path planning problem by inspiring animal mode of foraging using Lévy distribution augmented with Bayesian reasoning was fully described in this paper. Results proof that the Lévy flight strategy performs better than the previous fixed-pattern (e.g., parallel sweeps) approaches in terms of energy and time utilisation. We also introduced a waypoint assessment strategy called k-previous waypoints assessment. It improves the performance of the ordinary levy flight by saving agent’s resources and mission time through redundant search avoidance. The agents (UAVs) are to report their mission knowledge at the central server for interpretation and prediction purposes. Bayesian reasoning and learning were used for the SA and results proof effectiveness in different environments scenario in terms of prediction and effective knowledge representation. The prediction accuracy was measured using learning error rate, logarithm loss, and Brier score and the result proves that little agents mission that can be used for prediction within the same or different environment. Finally, we described a situation-based knowledge visualization and prediction technique for heterogeneous multi-UAV mission. While this paper proves linkage of Bayesian reasoning and learning with SA and effective searching strategy, future works is focusing on simplifying the architecture.

Keywords: Levy flight, distributed constraint optimization problem, multi-agent system, multi-robot coordination, autonomous system, swarm intelligence

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Authors: Farzaneh Sarbandi Farahani

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Drought and decrease in the level of lakes in recent years due to global warming and excessive use of water resources feeding lakes are of great importance, and this research has provided a structure to investigate this issue. First, the information required for simulating lake drought is provided with strong references and necessary assumptions. Entity-Component-System (ECS) structure has been used for simulation, which can consider assumptions flexibly in simulation. Three major users (i.e., Industry, agriculture, and Domestic users) consume water from groundwater and surface water (i.e., streams, rivers and lakes). Lake Mead has been considered for simulation, and the information necessary to investigate its drought has also been provided. The results are presented in the form of a scenario-based design and optimal strategy selection. For optimal strategy selection, a deep reinforcement algorithm is developed to select the best set of strategies among all possible projects. These results can provide a better view of how to plan to prevent lake drought.

Keywords: drought simulation, Mead lake, entity component system programming, deep reinforcement learning

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Authors: Asena Biber, Ates Gul Ergun, Seda Bulut

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Based on the literature, there are large numbers of studies investigating the relationship between culture and leadership effectiveness. Although giving effective speeches is vital characteristic for a leader to be perceived as effective, to our knowledge, there is no research study the determinants of perceived effective leader speech. The aim of this study is to find the effects of both culture and voice pitch on perceptions of leader's speech effectiveness. Our hypothesis is that people from high power distance countries will perceive leaders' speech effective when the leader's voice pitch is high, comparing with people from relatively low power distance countries. The participants of the study were 36 undergraduate students (12 Pakistanis, 12 Nigerians, and 12 Turks) who are studying in Turkey. National power distance scores of Nigerians ranked as first, Turks ranked as second and Pakistanis ranked as third. There are two independent variables in this study; three nationality groups that representing three levels of power distance and voice pitch of the leader which is manipulated as high and low levels. Researchers prepared an audio to manipulate high and low conditions of voice pitch. A professional whose native language is English read the predetermined speech in high and low voice pitch conditions. Voice pitch was measured using Hertz (Hz) and Decibel (dB). Each nationality group (Pakistan, Nigeria, and Turkey) were divided into groups of six students who listened to either the low or high pitch conditions in the cubicles of the laboratory. It was expected from participants to listen to the audio and fill in the questionnaire which was measuring the leadership effectiveness on a response scale ranging from 1 to 5. To determine the effects of nationality and voice pitch on perceived effectiveness of leader' voice pitch, 3 (Pakistani, Nigerian, and Turk) x 2 (low voice pitch and high voice pitch) two way between subjects analysis of variances was carried out. The results indicated that there was no significant main effect of voice pitch and interaction effect on perceived effectiveness of the leader’s voice pitch. However, there was a significant main effect of nationality on perceived effectiveness of the leader's voice pitch. Based on the results of Turkey’s HSD post-hoc test, only the perceived effectiveness of the leader's speech difference between Pakistanis and Nigerians was statistically significant. The results show that the hypothesis of this study was not supported. As limitations of the study, it is of importance to mention that the sample size should be bigger. Also, the language of the questionnaire and speech should be in the participant’s native language in further studies.

Keywords: culture, leadership effectiveness, power distance, voice pitch

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Authors: Leticia Souza Guirra, Márcia Eduarda Vieira Ramos, Edlaine Souza Pereira, Leticia Correa Celeste

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Introduction: Studies indicate that equine-assisted therapy enables improvements in several areas of functioning that are impaired in children with autism spectrum disorder (ASD), such as social interaction and communication. Objective: The study proposes to analyze the development of dialogic skills of a verbal child with ASD after participating in the equine-assisted therapy Step By Step in Communication. Method: This is quantitative and qualitative research through a case study. It refers to a 6 years old child diagnosed with ASD belonging to a group of practitioners of the Brazilian National Equine-Assited-Therapy Association. The Behavioral Observation Protocol (PROC) was used to evaluate communicative skills before and after the intervention, which consisted of 24 sessions once a week. Results: All conversational skills increased their frequency, with participation in dialogue and initiation of interaction. The child also increases the habit of waiting for his turn and answering the interlocutor. The emission of topics not related to conversation and echolalia showed a significant decrease after the intervention. Conclusion: The studied child showed improvement in communicative skills after participating in the equine-assisted therapy Step By Step in Communication. Contributions: This study contributes to a greater understanding of the impact of equine-assisted therapy on the communicative abilities of children with ASD.

Keywords: equine-assisted-therapy, autism spectrum disorder, language, communication, language and hearing sciences

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Authors: A. Marais

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A challenging aspect of teaching cello to novice adult learners is finding adequate lesson material and applying relevant teaching methodologies. It could play a crucial role in adult learners' decision to commence or stop taking music lessons. This study contributes to the theory and practises of continuing education. This study is important to lifelong learning, especially with the focus on adult teaching and learning and the difficulties concerning these themes. The research problem identified for this study is we are not aware of adults' life stories; thus, cello lesson material is not always relevant for adult's specific needs for motivation and goals for starting cello lessons. In my experience, an adult does not necessarily want to play children songs when they learn a new instrument. They want material and lessons fitted to adult learners. Adults also learn differently from younger beginners. Adults ask questions such as how and why, while children more readily accept what is being taught. This research creates awareness of adults' musical needs and learning methods. If every adult shares their own story for commencing and continuing with cello lessons, material should be created, revised, or adapted for more individually appropriate lessons. A number of studies show that adults taking music lessons experience a decrease in feelings of loneliness and isolation. It gives adults a sense of wellbeing and can help improve immune systems. The purpose of this research study will be to discover the life stories of adult amateur cellists. At this stage in the research, the life stories of amateur cellists can generally be defined as personal reflections of their motivations for and experiences of commencing and continuing with individual lessons. The findings of this study will contribute to the development of cello lesson material for adult beginners based on their stories. This research could also encourage adults to commence with music lessons and could, in that way, contribute to their quality of life. Music learners become aware of deep spiritual, emotional, and social values incorporated or experienced through musical learning. This will be a qualitative study with a narrative approach making use of oral history. The chosen method will encapsulate the stories of amateur individual adults starting and continuing with cello lessons. The narrative method entails experiences as expressed in lived and told stories of individuals. Oral history is used as part of the narrative method and entails gathering of personal reflections of events and their cause and effects from an individual or several individuals. These findings from this study will contribute to adult amateur cellists' motivations to continue with music lessons and inspire others to commence. The inspiring life stories of the amateur cellists would provide insight into finding and creating new cello lesson material and enhance existing teaching methodologies for adult amateur cellists.

Keywords: adult, amateur, cello, education, learning, music, stories

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Authors: Pantaleon Lutta, Mohamed Sedky, Mohamed Hassan

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The inability of organizations to put in place management control measures for Internet of Things (IoT) complexities persists to be a risk concern. Policy makers have been left to scamper in finding measures to combat these security and privacy concerns. IoT forensics is a cumbersome process as there is no standardization of the IoT products, no or limited historical data are stored on the devices. This paper highlights why IoT forensics is a unique adventure and brought out the legal challenges encountered in the investigation process. A quadrant model is presented to study the conflicting aspects in IoT forensics. The model analyses the effectiveness of forensic investigation process versus the admissibility of the evidence integrity; taking into account the user privacy and the providers’ compliance with the laws and regulations. Our analysis concludes that a semi-automated forensic process using machine learning, could eliminate the human factor from the profiling and surveillance processes, and hence resolves the issues of data protection (privacy and confidentiality).

Keywords: cloud forensics, data protection Laws, GDPR, IoT forensics, machine Learning

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Authors: Cina Mosito, Toyin Mary Adewumi, Charlene Nissen

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One of the goals of inclusive education is to provide learners with suitable learning environments and prospects to best attain their potential. This study sought to determine the experiences of studying inclusive education on pre-service teachers’ teaching within the South African education context. A purposeful sample comprising 6 pre-service teachers was selected from a university of technology located in the Western Cape South Africa. Data were collected using open-ended questionnaires, which were exploratory in nature and analyzed thematically. The findings supported significant proportions of experiences as self-reported by pre-service teachers. The pre-service teachers’ experiences of studying inclusive education included inclusive education as an “eye-opener” to the fact that learners experiencing various barriers to learning can be accommodated in the regular classrooms, exposure to some aspects of inclusive education, such as diversity, learners’ rights, and curriculum differentiation. It was also revealed that studying inclusive education made pre-service teachers love and enjoy teaching more. The study shows that awareness of inclusive education has influenced pre-service teachers in South African schools.

Keywords: experience, inclusive education, pre-service teacher, South Africa

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Authors: Anne Marie Morrin

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This paper will focus on the design, delivery and assessment of a transdisciplinary STEAM (Science, Technology, Engineering, Arts, and Mathematics) education initiative in a college of education in Ireland. The project explores a transdisciplinary approach to supporting STEAM education where the concepts, methodologies and assessments employed derive from visual art sessions within initial teacher education. The research will demonstrate that the STEAM Education approach is effective when visual art concepts and methods are placed at the core of the teaching and learning experience. Within this study, emphasis is placed on authentic collaboration and transdisciplinary pedagogical approaches with the STEAM subjects. The partners included a combination of teaching expertise in STEM and Visual Arts education, artists, in-service and pre-service teachers and children. The inclusion of all stakeholders mentioned moves towards a more authentic approach where transdisciplinary practice is at the core of the teaching and learning. Qualitative data was collected using a combination of questionnaires (focused and open-ended questions) and focus groups. In addition, the data was collected through video diaries where students reflected on their visual journals and transdisciplinary practice, which gave rich insight into participants' experiences and opinions on their learning. It was found that an effective program of STEAM education integration was informed by co-teaching (continuous professional development), which involved a commitment to adaptable and flexible approaches to teaching, learning, and assessment, as well as the importance of continuous reflection-in-action by all participants. The delivery of a transdisciplinary model of STEAM education was devised to reconceptualizatise how individual subject areas can develop essential skills and tackle critical issues (such as self-care and climate change) through data visualisation and technology. The success of the project can be attributed to the collaboration, which was inclusive, flexible and a willingness between various stakeholders to be involved in the design and implementation of the project from conception to completion. The case study approach taken is particularistic (focusing on the STEAM-ED project), descriptive (providing in-depth descriptions from varied and multiple perspectives), and heuristic (interpreting the participants’ experiences and what meaning they attributed to their experiences).

Keywords: collaboration, transdisciplinary, STEAM, visual arts education

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Authors: Christine Demaecker

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Many researchers have underlined the importance of metaphors in specialised language. Their use of specific domains helps us understand the conceptualisations used to communicate new ideas or difficult topics. Within the wide area of specialised discourse, wine tasting is a very specific example because it is almost exclusively metaphoric. Wine tasting metaphors express various conceptualisations. They are not linguistic but rather conceptual, as defined by Lakoff & Johnson. They correspond to the linguistic expression of a mental projection from a well-known or more concrete source domain onto the target domain, which is the taste of wine. But unlike most specialised terminologies, the vocabulary is never clearly defined. When metaphorical terms are listed in dictionaries, their definitions remain vague, unclear, and circular. They cannot be replaced by literal linguistic expressions. This makes it impossible to transfer them into another language with the traditional linguistic translation methods. Qualitative research investigates whether wine tasting metaphors could rather be translated with the cognitive translation process, as well described by Nili Mandelblit (1995). The research is based on a corpus compiled from two high-profile wine guides; the Parker’s Wine Buyer’s Guide and its translation into French and the Guide Hachette des Vins and its translation into English. In this small corpus with a total of 68,826 words, 170 metaphoric expressions have been identified in the original English text and 180 in the original French text. They have been selected with the MIPVU Metaphor Identification Procedure developed at the Vrije Universiteit Amsterdam. The selection demonstrates that both languages use the same set of conceptualisations, which are often combined in wine tasting notes, creating conceptual integrations or blends. The comparison of expressions in the source and target texts also demonstrates the use of the cognitive translation approach. In accordance with the principle of relevance, the translation always uses target language conceptualisations, but compared to the original, the highlighting of the projection is often different. Also, when original metaphors are complex with a combination of conceptualisations, at least one element of the original metaphor underlies the target expression. This approach perfectly integrates into Lederer’s interpretative model of translation (2006). In this triangular model, the transfer of conceptualisation could be included at the level of ‘deverbalisation/reverbalisation’, the crucial stage of the model, where the extraction of meaning combines with the encyclopedic background to generate the target text.

Keywords: cognitive translation, conceptual integration, conceptual metaphor, interpretative model of translation, wine tasting metaphor

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Authors: Yaowei Hu, Yongkai Wu, Lu Zhang, Xintao Wu

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Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounders. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, with an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make any assumption about the data generating process and the type of the variables. Experiments using both synthetic and real-world datasets show the effectiveness of the method.

Keywords: average causal effect, hidden confounding, bound estimation, generative adversarial learning

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Authors: Sina Saadati

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Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems.

Keywords: task scheduling, MOSOC, artificial neural network, machine learning, architecture of computers, artificial intelligence

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Authors: Nargiz Ismayilova

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The issue of modernism and the concept of postmodernism has been the focus of world researchers for many years, and there are very few researchers who have come to a common denominator about this term. During the independence period, the expansion of the relations of Azerbaijani literature with the world has led to the spread of many currents and tendencies formed in the West to the literary environment in our country. In this context, the works created in our environment are distinguished by their extreme richness in terms of subject matter and diversity in terms of genre. As an interesting example of contemporary postmodern prose in Azerbaijan, Parviz Jabrayil's novel "In a Foreign Language" pays attention with its more different plotline. The disagreement exists among the critics about the novel. Some are looking for high artistry in work; others are satisfied with the elements of postmodernism in work. Delimitation of the border between modernism and postmodernism can serve to carry out a deep scientific study of the novel. The novel depicts the world in the author's consciousness against the background of water shortage (thirst) in the Old City (Icharishahar). The author deconstructs today's Ichari Shahar mould. Along with modernism, elements of postmodernism occupy a large place in the work. When we look at the general tendencies of postmodernist art, we see that science and individuality are questioned, criticizing the sharp boundaries of modernism and the negativity of these restrictions, and modernism offers alternatives to artistic production by identifying its negatives and shortcomings in the areas of artistic freedom. The novel is extremely interesting in this point of view.

Keywords: concept of postmodernism, modernism, delimitation, political postmodernism, modern postmodern prose, Azerbaijani literature, novel, comparison, world literature, analysis

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